{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N2S9OhvtGBgS"
   },
   "source": [
    "# 基于 TensorFlow 在 SecretFlow 中实现水平联邦图像分割任务\n",
    "\n",
    "## 引言\n",
    "\n",
    "本教程基于 TensorFlow 的 [使用 BASNet 进行图像分割教程](https://keras.io/examples/vision/basnet_segmentation/) 而改写，通过本教程，您将了解到现有的基于 TensorFlow 的示例如何快速地迁移到 SecretFlow 隐语的联邦学习框架之下，实现模型的联邦学习化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JU8lKQ_zGBgg"
   },
   "source": [
    "## 单机模式\n",
    "\n",
    "### 小节引言\n",
    "本小节的代码主要来自于 [Highly accurate boundaries segmentation using BASNet](https://keras.io/examples/vision/basnet_segmentation/) ，主要讲解如何在 Keras 下实现边界感知分割网络（BASNet），通过使用两阶段预测和细化架构，以及混合损失，去预测高精度的边界和图像分割的精细结构。 处于演示效率的考虑，在本次示例，教程通过在数据集 [DUTS-TE](http://saliencydetection.net/duts/) 上使用 140 张图片用来训练和测试，实际上，DUTS是比较大的突出对象分割数据集。 为了教程的简洁，本小节仅仅简要介绍了一下各部分的功能；对于实现的具体解析，请读者移步参考[原教程](https://keras.io/examples/vision/basnet_segmentation/)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UYR2p-a_GBgh"
   },
   "source": [
    "### 下载数据\n",
    "\n",
    "第一次运行本教程时，将通过下面的单元格下载数据。往后再次运行时，请注释掉下列语句，避免数据的重复下载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "eEEgau_WGBgi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-10-22 03:44:18--  http://saliencydetection.net/duts/download/DUTS-TE.zip\n",
      "Resolving saliencydetection.net (saliencydetection.net)... 36.55.239.177\n",
      "Connecting to saliencydetection.net (saliencydetection.net)|36.55.239.177|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 139799089 (133M) [application/zip]\n",
      "Saving to: ‘DUTS-TE.zip’\n",
      "\n",
      "DUTS-TE.zip         100%[===================>] 133.32M  4.12MB/s    in 30s     \n",
      "\n",
      "2023-10-22 03:44:49 (4.37 MB/s) - ‘DUTS-TE.zip’ saved [139799089/139799089]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://saliencydetection.net/duts/download/DUTS-TE.zip\n",
    "!unzip -q DUTS-TE.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "azxysX86GBgk"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-14 08:02:27.501730: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-10-14 08:02:27.678637: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-10-14 08:02:27.683847: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-10-14 08:02:27.683869: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-10-14 08:02:29.317935: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-10-14 08:02:29.318034: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-10-14 08:02:29.318045: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
      "/opt/anaconda3/envs/limingbo_sf/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import keras_cv\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ROFECOoyGBgk"
   },
   "source": [
    "### 定义超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "j-CajS8CGBgl"
   },
   "outputs": [],
   "source": [
    "IMAGE_SIZE = 288\n",
    "BATCH_SIZE = 4\n",
    "OUT_CLASSES = 1\n",
    "TRAIN_SPLIT_RATIO = 0.90\n",
    "DATA_DIR = \"./DUTS-TE/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dQObi6QqGBgl"
   },
   "source": [
    "### 创建 TensorFlow 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "L3_Nh7zCGBgm"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/anaconda3/envs/limingbo_sf/lib/python3.8/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n",
      "Train Dataset: <PrefetchDataset element_spec=(TensorSpec(shape=(None, 288, 288, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 288, 288, 1), dtype=tf.float32, name=None))>\n",
      "Validation Dataset: <PrefetchDataset element_spec=(TensorSpec(shape=(None, 288, 288, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 288, 288, 1), dtype=tf.float32, name=None))>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-14 08:02:33.958405: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2023-10-14 08:02:33.958447: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: luoyegroup-ubuntu\n",
      "2023-10-14 08:02:33.958455: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: luoyegroup-ubuntu\n",
      "2023-10-14 08:02:33.958597: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 535.104.5\n",
      "2023-10-14 08:02:33.958621: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 535.104.5\n",
      "2023-10-14 08:02:33.958627: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 535.104.5\n",
      "2023-10-14 08:02:33.958993: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "def load_paths(path, split_ratio):\n",
    "    images = sorted(glob(os.path.join(path, \"DUTS-TE-Image/*\")))[:140]\n",
    "    masks = sorted(glob(os.path.join(path, \"DUTS-TE-Mask/*\")))[:140]\n",
    "\n",
    "    len_ = int(len(images) * split_ratio)\n",
    "    return (images[:len_], masks[:len_]), (images[len_:], masks[len_:])\n",
    "\n",
    "\n",
    "def read_image(path, size, mode):\n",
    "    x = keras.utils.load_img(path, target_size=size, color_mode=mode)\n",
    "    x = keras.utils.img_to_array(x)\n",
    "    x = (x / 255.0).astype(np.float32)\n",
    "    return x\n",
    "\n",
    "\n",
    "def preprocess(x_batch, y_batch, img_size, out_classes):\n",
    "    def f(_x, _y):\n",
    "        _x, _y = _x.decode(), _y.decode()\n",
    "        _x = read_image(_x, (img_size, img_size), mode=\"rgb\")  # image\n",
    "        _y = read_image(_y, (img_size, img_size), mode=\"grayscale\")  # mask\n",
    "        return _x, _y\n",
    "\n",
    "    images, masks = tf.numpy_function(f, [x_batch, y_batch], [tf.float32, tf.float32])\n",
    "    images.set_shape([img_size, img_size, 3])\n",
    "    masks.set_shape([img_size, img_size, out_classes])\n",
    "    return images, masks\n",
    "\n",
    "\n",
    "def load_dataset(image_paths, mask_paths, img_size, out_classes, batch, shuffle=True):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((image_paths, mask_paths))\n",
    "    if shuffle:\n",
    "        dataset = dataset.cache().shuffle(buffer_size=1000)\n",
    "    dataset = dataset.map(\n",
    "        lambda x, y: preprocess(x, y, img_size, out_classes),\n",
    "        num_parallel_calls=tf.data.AUTOTUNE,\n",
    "    )\n",
    "    dataset = dataset.batch(batch)\n",
    "    dataset = dataset.prefetch(tf.data.AUTOTUNE)\n",
    "    return dataset\n",
    "\n",
    "\n",
    "train_paths, val_paths = load_paths(DATA_DIR, TRAIN_SPLIT_RATIO)\n",
    "\n",
    "train_dataset = load_dataset(\n",
    "    train_paths[0], train_paths[1], IMAGE_SIZE, OUT_CLASSES, BATCH_SIZE, shuffle=True\n",
    ")\n",
    "val_dataset = load_dataset(\n",
    "    val_paths[0], val_paths[1], IMAGE_SIZE, OUT_CLASSES, BATCH_SIZE, shuffle=False\n",
    ")\n",
    "\n",
    "print(f\"Train Dataset: {train_dataset}\")\n",
    "print(f\"Validation Dataset: {val_dataset}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gCtH19ibGBgn"
   },
   "source": [
    "### 可视化数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "L_fkE6YCGBgn"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display(display_list):\n",
    "    title = [\"Input Image\", \"True Mask\", \"Predicted Mask\"]\n",
    "\n",
    "    for i in range(len(display_list)):\n",
    "        plt.subplot(1, len(display_list), i + 1)\n",
    "        plt.title(title[i])\n",
    "        plt.imshow(keras.utils.array_to_img(display_list[i]), cmap=\"gray\")\n",
    "        plt.axis(\"off\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "for image, mask in val_dataset.take(1):\n",
    "    display([image[0], mask[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yJleGX--GBgn"
   },
   "source": [
    "### 分析掩码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "GtR1sA7TGBgo"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values count: 245\n",
      "Unique values:\n",
      "[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  61  62  63  65  66  67  68  69  70  71  73  74\n",
      "  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92\n",
      "  93  94  95  96  97  98  99 100 101 102 103 104 105 108 109 110 111 112\n",
      " 113 114 115 116 117 118 119 120 122 123 124 125 128 129 130 131 132 133\n",
      " 134 135 136 137 138 139 140 141 142 144 145 146 147 148 149 150 151 152\n",
      " 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 170 171\n",
      " 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189\n",
      " 190 191 192 193 194 195 196 197 198 199 201 202 203 204 205 206 207 208\n",
      " 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226\n",
      " 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244\n",
      " 245 246 247 248 249 250 251 252 253 254 255]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Unique values count: {len(np.unique((mask[0] * 255)))}\")\n",
    "print(\"Unique values:\")\n",
    "print(np.unique((mask[0] * 255)).astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6zEfc-jOGBgo"
   },
   "source": [
    "### 构建边界感知分割网络（BASNet）模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "dQqXO4jUGBgp"
   },
   "outputs": [],
   "source": [
    "def basic_block(x_input, filters, stride=1, down_sample=None, activation=None):\n",
    "    \"\"\"Creates a residual(identity) block with two 3*3 convolutions.\"\"\"\n",
    "    residual = x_input\n",
    "\n",
    "    x = layers.Conv2D(filters, (3, 3), strides=stride, padding=\"same\", use_bias=False)(\n",
    "        x_input\n",
    "    )\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    x = layers.Conv2D(filters, (3, 3), strides=(1, 1), padding=\"same\", use_bias=False)(\n",
    "        x\n",
    "    )\n",
    "    x = layers.BatchNormalization()(x)\n",
    "\n",
    "    if down_sample is not None:\n",
    "        residual = down_sample\n",
    "\n",
    "    x = layers.Add()([x, residual])\n",
    "\n",
    "    if activation is not None:\n",
    "        x = layers.Activation(activation)(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "def convolution_block(x_input, filters, dilation=1):\n",
    "    \"\"\"Apply convolution + batch normalization + relu layer.\"\"\"\n",
    "    x = layers.Conv2D(filters, (3, 3), padding=\"same\", dilation_rate=dilation)(x_input)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    return layers.Activation(\"relu\")(x)\n",
    "\n",
    "\n",
    "def segmentation_head(x_input, out_classes, final_size):\n",
    "    \"\"\"Map each decoder stage output to model output classes.\"\"\"\n",
    "    x = layers.Conv2D(out_classes, kernel_size=(3, 3), padding=\"same\")(x_input)\n",
    "\n",
    "    if final_size is not None:\n",
    "        x = layers.Resizing(final_size[0], final_size[1])(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "def get_resnet_block(_resnet, block_num):\n",
    "    \"\"\"Extract and return ResNet-34 block.\"\"\"\n",
    "    resnet_layers = [3, 4, 6, 3]  # ResNet-34 layer sizes at different block.\n",
    "    return keras.models.Model(\n",
    "        inputs=_resnet.get_layer(f\"v2_stack_{block_num}_block1_1_conv\").input,\n",
    "        outputs=_resnet.get_layer(\n",
    "            f\"v2_stack_{block_num}_block{resnet_layers[block_num]}_add\"\n",
    "        ).output,\n",
    "        name=f\"resnet34_block{block_num + 1}\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pHMUkGHVGBgp"
   },
   "source": [
    "### 预测模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Qz58ZGBOGBgp"
   },
   "outputs": [],
   "source": [
    "def basnet_predict(input_shape, out_classes):\n",
    "    \"\"\"BASNet Prediction Module, it outputs coarse label map.\"\"\"\n",
    "    filters = 64\n",
    "    num_stages = 6\n",
    "\n",
    "    x_input = layers.Input(input_shape)\n",
    "\n",
    "    # -------------Encoder--------------\n",
    "    x = layers.Conv2D(filters, kernel_size=(3, 3), padding=\"same\")(x_input)\n",
    "\n",
    "    resnet = keras_cv.models.ResNet34Backbone(\n",
    "        include_rescaling=False,\n",
    "    )\n",
    "\n",
    "    encoder_blocks = []\n",
    "    for i in range(num_stages):\n",
    "        if i < 4:  # First four stages are adopted from ResNet-34 blocks.\n",
    "            x = get_resnet_block(resnet, i)(x)\n",
    "            encoder_blocks.append(x)\n",
    "            x = layers.Activation(\"relu\")(x)\n",
    "        else:  # Last 2 stages consist of three basic resnet blocks.\n",
    "            x = layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2))(x)\n",
    "            x = basic_block(x, filters=filters * 8, activation=\"relu\")\n",
    "            x = basic_block(x, filters=filters * 8, activation=\"relu\")\n",
    "            x = basic_block(x, filters=filters * 8, activation=\"relu\")\n",
    "            encoder_blocks.append(x)\n",
    "\n",
    "    # -------------Bridge-------------\n",
    "    x = convolution_block(x, filters=filters * 8, dilation=2)\n",
    "    x = convolution_block(x, filters=filters * 8, dilation=2)\n",
    "    x = convolution_block(x, filters=filters * 8, dilation=2)\n",
    "    encoder_blocks.append(x)\n",
    "\n",
    "    # -------------Decoder-------------\n",
    "    decoder_blocks = []\n",
    "    for i in reversed(range(num_stages)):\n",
    "        if i != (num_stages - 1):  # Except first, scale other decoder stages.\n",
    "            shape = keras.backend.int_shape(x)\n",
    "            x = layers.Resizing(shape[1] * 2, shape[2] * 2)(x)\n",
    "\n",
    "        x = layers.concatenate([encoder_blocks[i], x], axis=-1)\n",
    "        x = convolution_block(x, filters=filters * 8)\n",
    "        x = convolution_block(x, filters=filters * 8)\n",
    "        x = convolution_block(x, filters=filters * 8)\n",
    "        decoder_blocks.append(x)\n",
    "\n",
    "    decoder_blocks.reverse()  # Change order from last to first decoder stage.\n",
    "    decoder_blocks.append(encoder_blocks[-1])  # Copy bridge to decoder.\n",
    "\n",
    "    # -------------Side Outputs--------------\n",
    "    decoder_blocks = [\n",
    "        segmentation_head(decoder_block, out_classes, input_shape[:2])\n",
    "        for decoder_block in decoder_blocks\n",
    "    ]\n",
    "\n",
    "    return keras.models.Model(inputs=[x_input], outputs=decoder_blocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vdZj1rdeGBgq"
   },
   "source": [
    "### 残差修正模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "03K7h2YwGBgq"
   },
   "outputs": [],
   "source": [
    "def basnet_rrm(base_model, out_classes):\n",
    "    \"\"\"BASNet Residual Refinement Module(RRM) module, output fine label map.\"\"\"\n",
    "    num_stages = 4\n",
    "    filters = 64\n",
    "\n",
    "    x_input = base_model.output[0]\n",
    "\n",
    "    # -------------Encoder--------------\n",
    "    x = layers.Conv2D(filters, kernel_size=(3, 3), padding=\"same\")(x_input)\n",
    "\n",
    "    encoder_blocks = []\n",
    "    for _ in range(num_stages):\n",
    "        x = convolution_block(x, filters=filters)\n",
    "        encoder_blocks.append(x)\n",
    "        x = layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2))(x)\n",
    "\n",
    "    # -------------Bridge--------------\n",
    "    x = convolution_block(x, filters=filters)\n",
    "\n",
    "    # -------------Decoder--------------\n",
    "    for i in reversed(range(num_stages)):\n",
    "        shape = keras.backend.int_shape(x)\n",
    "        x = layers.Resizing(shape[1] * 2, shape[2] * 2)(x)\n",
    "        x = layers.concatenate([encoder_blocks[i], x], axis=-1)\n",
    "        x = convolution_block(x, filters=filters)\n",
    "\n",
    "    x = segmentation_head(x, out_classes, None)  # Segmentation head.\n",
    "\n",
    "    # ------------- refined = coarse + residual\n",
    "    x = layers.Add()([x_input, x])  # Add prediction + refinement output\n",
    "\n",
    "    return keras.models.Model(inputs=[base_model.input], outputs=[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nx6K3C4WGBgq"
   },
   "source": [
    "### 组合预测模块和修正模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "0NpFo3EXGBgr"
   },
   "outputs": [],
   "source": [
    "def basnet(input_shape, out_classes):\n",
    "    \"\"\"BASNet, it's a combination of two modules\n",
    "    Prediction Module and Residual Refinement Module(RRM).\"\"\"\n",
    "\n",
    "    # Prediction model.\n",
    "    predict_model = basnet_predict(input_shape, out_classes)\n",
    "    # Refinement model.\n",
    "    refine_model = basnet_rrm(predict_model, out_classes)\n",
    "\n",
    "    output = [refine_model.output]  # Combine outputs.\n",
    "    output.extend(predict_model.output)\n",
    "\n",
    "    output = [layers.Activation(\"sigmoid\")(_) for _ in output]  # Activations.\n",
    "\n",
    "    return keras.models.Model(inputs=[predict_model.input], outputs=output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kUkxEdeXGBgr"
   },
   "source": [
    "### 混合损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "MFxUmnlfGBgr"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 288, 288, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 288, 288, 64  1792        ['input_1[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " resnet34_block1 (Functional)   (None, None, None,   222720      ['conv2d[0][0]']                 \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 288, 288, 64  0           ['resnet34_block1[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " resnet34_block2 (Functional)   (None, None, None,   1118720     ['activation[0][0]']             \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 144, 144, 12  0           ['resnet34_block2[0][0]']        \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " resnet34_block3 (Functional)   (None, None, None,   6829056     ['activation_1[0][0]']           \n",
      "                                256)                                                              \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 72, 72, 256)  0           ['resnet34_block3[0][0]']        \n",
      "                                                                                                  \n",
      " resnet34_block4 (Functional)   (None, None, None,   13121536    ['activation_2[0][0]']           \n",
      "                                512)                                                              \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 36, 36, 512)  0           ['resnet34_block4[0][0]']        \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 18, 18, 512)  0           ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 18, 18, 512)  2359296     ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 18, 18, 512)  2048       ['conv2d_1[0][0]']               \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 18, 18, 512)  0           ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 18, 18, 512)  2359296     ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 18, 18, 512)  2048       ['conv2d_2[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 18, 18, 512)  0           ['batch_normalization_1[0][0]',  \n",
      "                                                                  'max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 18, 18, 512)  0           ['add[0][0]']                    \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 18, 18, 512)  2359296     ['activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 18, 18, 512)  2048       ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 18, 18, 512)  0           ['batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 18, 18, 512)  2359296     ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 18, 18, 512)  2048       ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 18, 18, 512)  0           ['batch_normalization_3[0][0]',  \n",
      "                                                                  'activation_5[0][0]']           \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 18, 18, 512)  0           ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 18, 18, 512)  2359296     ['activation_7[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 18, 18, 512)  2048       ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 18, 18, 512)  0           ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 18, 18, 512)  2359296     ['activation_8[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 18, 18, 512)  2048       ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_2 (Add)                    (None, 18, 18, 512)  0           ['batch_normalization_5[0][0]',  \n",
      "                                                                  'activation_7[0][0]']           \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 18, 18, 512)  0           ['add_2[0][0]']                  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 9, 9, 512)   0           ['activation_9[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 9, 9, 512)    2359296     ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 9, 9, 512)   2048        ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 9, 9, 512)    2359296     ['activation_10[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 9, 9, 512)   2048        ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_3 (Add)                    (None, 9, 9, 512)    0           ['batch_normalization_7[0][0]',  \n",
      "                                                                  'max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 9, 9, 512)    0           ['add_3[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 9, 9, 512)    2359296     ['activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 9, 9, 512)   2048        ['conv2d_9[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 9, 9, 512)    2359296     ['activation_12[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 9, 9, 512)   2048        ['conv2d_10[0][0]']              \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_4 (Add)                    (None, 9, 9, 512)    0           ['batch_normalization_9[0][0]',  \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 9, 9, 512)    0           ['add_4[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 9, 9, 512)    2359296     ['activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 9, 9, 512)   2048        ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 9, 9, 512)    2359296     ['activation_14[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 9, 9, 512)   2048        ['conv2d_12[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " add_5 (Add)                    (None, 9, 9, 512)    0           ['batch_normalization_11[0][0]', \n",
      "                                                                  'activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 9, 9, 512)    0           ['add_5[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 9, 9, 512)    2359808     ['activation_15[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 9, 9, 512)   2048        ['conv2d_13[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 9, 9, 512)    2359808     ['activation_16[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 9, 9, 512)   2048        ['conv2d_14[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_13[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, 9, 9, 512)    2359808     ['activation_17[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 9, 9, 512)   2048        ['conv2d_15[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_14[0][0]'] \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 9, 9, 1024)   0           ['activation_15[0][0]',          \n",
      "                                                                  'activation_18[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 9, 9, 512)    4719104     ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 9, 9, 512)   2048        ['conv2d_16[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_15[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 9, 9, 512)    2359808     ['activation_19[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, 9, 9, 512)   2048        ['conv2d_17[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_16[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 9, 9, 512)    2359808     ['activation_20[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, 9, 9, 512)   2048        ['conv2d_18[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, 9, 9, 512)    0           ['batch_normalization_17[0][0]'] \n",
      "                                                                                                  \n",
      " resizing (Resizing)            (None, 18, 18, 512)  0           ['activation_21[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 18, 18, 1024  0           ['activation_9[0][0]',           \n",
      "                                )                                 'resizing[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, 18, 18, 512)  4719104     ['concatenate_1[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, 18, 18, 512)  2048       ['conv2d_19[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, 18, 18, 512)  0           ['batch_normalization_18[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)             (None, 18, 18, 512)  2359808     ['activation_22[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, 18, 18, 512)  2048       ['conv2d_20[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, 18, 18, 512)  0           ['batch_normalization_19[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, 18, 18, 512)  2359808     ['activation_23[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, 18, 18, 512)  2048       ['conv2d_21[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, 18, 18, 512)  0           ['batch_normalization_20[0][0]'] \n",
      "                                                                                                  \n",
      " resizing_1 (Resizing)          (None, 36, 36, 512)  0           ['activation_24[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, 36, 36, 1024  0           ['resnet34_block4[0][0]',        \n",
      "                                )                                 'resizing_1[0][0]']             \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, 36, 36, 512)  4719104     ['concatenate_2[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, 36, 36, 512)  2048       ['conv2d_22[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, 36, 36, 512)  0           ['batch_normalization_21[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)             (None, 36, 36, 512)  2359808     ['activation_25[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, 36, 36, 512)  2048       ['conv2d_23[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, 36, 36, 512)  0           ['batch_normalization_22[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)             (None, 36, 36, 512)  2359808     ['activation_26[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, 36, 36, 512)  2048       ['conv2d_24[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, 36, 36, 512)  0           ['batch_normalization_23[0][0]'] \n",
      "                                                                                                  \n",
      " resizing_2 (Resizing)          (None, 72, 72, 512)  0           ['activation_27[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate)    (None, 72, 72, 768)  0           ['resnet34_block3[0][0]',        \n",
      "                                                                  'resizing_2[0][0]']             \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)             (None, 72, 72, 512)  3539456     ['concatenate_3[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, 72, 72, 512)  2048       ['conv2d_25[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, 72, 72, 512)  0           ['batch_normalization_24[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)             (None, 72, 72, 512)  2359808     ['activation_28[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, 72, 72, 512)  2048       ['conv2d_26[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, 72, 72, 512)  0           ['batch_normalization_25[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)             (None, 72, 72, 512)  2359808     ['activation_29[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, 72, 72, 512)  2048       ['conv2d_27[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, 72, 72, 512)  0           ['batch_normalization_26[0][0]'] \n",
      "                                                                                                  \n",
      " resizing_3 (Resizing)          (None, 144, 144, 51  0           ['activation_30[0][0]']          \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " concatenate_4 (Concatenate)    (None, 144, 144, 64  0           ['resnet34_block2[0][0]',        \n",
      "                                0)                                'resizing_3[0][0]']             \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)             (None, 144, 144, 51  2949632     ['concatenate_4[0][0]']          \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, 144, 144, 51  2048       ['conv2d_28[0][0]']              \n",
      " ormalization)                  2)                                                                \n",
      "                                                                                                  \n",
      " activation_31 (Activation)     (None, 144, 144, 51  0           ['batch_normalization_27[0][0]'] \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)             (None, 144, 144, 51  2359808     ['activation_31[0][0]']          \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, 144, 144, 51  2048       ['conv2d_29[0][0]']              \n",
      " ormalization)                  2)                                                                \n",
      "                                                                                                  \n",
      " activation_32 (Activation)     (None, 144, 144, 51  0           ['batch_normalization_28[0][0]'] \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)             (None, 144, 144, 51  2359808     ['activation_32[0][0]']          \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, 144, 144, 51  2048       ['conv2d_30[0][0]']              \n",
      " ormalization)                  2)                                                                \n",
      "                                                                                                  \n",
      " activation_33 (Activation)     (None, 144, 144, 51  0           ['batch_normalization_29[0][0]'] \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " resizing_4 (Resizing)          (None, 288, 288, 51  0           ['activation_33[0][0]']          \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " concatenate_5 (Concatenate)    (None, 288, 288, 57  0           ['resnet34_block1[0][0]',        \n",
      "                                6)                                'resizing_4[0][0]']             \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)             (None, 288, 288, 51  2654720     ['concatenate_5[0][0]']          \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, 288, 288, 51  2048       ['conv2d_31[0][0]']              \n",
      " ormalization)                  2)                                                                \n",
      "                                                                                                  \n",
      " activation_34 (Activation)     (None, 288, 288, 51  0           ['batch_normalization_30[0][0]'] \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)             (None, 288, 288, 51  2359808     ['activation_34[0][0]']          \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, 288, 288, 51  2048       ['conv2d_32[0][0]']              \n",
      " ormalization)                  2)                                                                \n",
      "                                                                                                  \n",
      " activation_35 (Activation)     (None, 288, 288, 51  0           ['batch_normalization_31[0][0]'] \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, 288, 288, 51  2359808     ['activation_35[0][0]']          \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, 288, 288, 51  2048       ['conv2d_33[0][0]']              \n",
      " ormalization)                  2)                                                                \n",
      "                                                                                                  \n",
      " activation_36 (Activation)     (None, 288, 288, 51  0           ['batch_normalization_32[0][0]'] \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, 288, 288, 1)  4609        ['activation_36[0][0]']          \n",
      "                                                                                                  \n",
      " resizing_5 (Resizing)          (None, 288, 288, 1)  0           ['conv2d_34[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)             (None, 288, 288, 64  640         ['resizing_5[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)             (None, 288, 288, 64  36928       ['conv2d_41[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, 288, 288, 64  256        ['conv2d_42[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_37 (Activation)     (None, 288, 288, 64  0           ['batch_normalization_33[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 144, 144, 64  0          ['activation_37[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)             (None, 144, 144, 64  36928       ['max_pooling2d_2[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, 144, 144, 64  256        ['conv2d_43[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_38 (Activation)     (None, 144, 144, 64  0           ['batch_normalization_34[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, 72, 72, 64)  0           ['activation_38[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)             (None, 72, 72, 64)   36928       ['max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, 72, 72, 64)  256         ['conv2d_44[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_39 (Activation)     (None, 72, 72, 64)   0           ['batch_normalization_35[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_4 (MaxPooling2D)  (None, 36, 36, 64)  0           ['activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)             (None, 36, 36, 64)   36928       ['max_pooling2d_4[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_36 (BatchN  (None, 36, 36, 64)  256         ['conv2d_45[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_40 (Activation)     (None, 36, 36, 64)   0           ['batch_normalization_36[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_5 (MaxPooling2D)  (None, 18, 18, 64)  0           ['activation_40[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)             (None, 18, 18, 64)   36928       ['max_pooling2d_5[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_37 (BatchN  (None, 18, 18, 64)  256         ['conv2d_46[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_41 (Activation)     (None, 18, 18, 64)   0           ['batch_normalization_37[0][0]'] \n",
      "                                                                                                  \n",
      " resizing_12 (Resizing)         (None, 36, 36, 64)   0           ['activation_41[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_6 (Concatenate)    (None, 36, 36, 128)  0           ['activation_40[0][0]',          \n",
      "                                                                  'resizing_12[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)             (None, 36, 36, 64)   73792       ['concatenate_6[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_38 (BatchN  (None, 36, 36, 64)  256         ['conv2d_47[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_42 (Activation)     (None, 36, 36, 64)   0           ['batch_normalization_38[0][0]'] \n",
      "                                                                                                  \n",
      " resizing_13 (Resizing)         (None, 72, 72, 64)   0           ['activation_42[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_7 (Concatenate)    (None, 72, 72, 128)  0           ['activation_39[0][0]',          \n",
      "                                                                  'resizing_13[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)             (None, 72, 72, 64)   73792       ['concatenate_7[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_39 (BatchN  (None, 72, 72, 64)  256         ['conv2d_48[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_43 (Activation)     (None, 72, 72, 64)   0           ['batch_normalization_39[0][0]'] \n",
      "                                                                                                  \n",
      " resizing_14 (Resizing)         (None, 144, 144, 64  0           ['activation_43[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " concatenate_8 (Concatenate)    (None, 144, 144, 12  0           ['activation_38[0][0]',          \n",
      "                                8)                                'resizing_14[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)             (None, 144, 144, 64  73792       ['concatenate_8[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_40 (BatchN  (None, 144, 144, 64  256        ['conv2d_49[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_44 (Activation)     (None, 144, 144, 64  0           ['batch_normalization_40[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " resizing_15 (Resizing)         (None, 288, 288, 64  0           ['activation_44[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " concatenate_9 (Concatenate)    (None, 288, 288, 12  0           ['activation_37[0][0]',          \n",
      "                                8)                                'resizing_15[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)             (None, 288, 288, 64  73792       ['concatenate_9[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_41 (BatchN  (None, 288, 288, 64  256        ['conv2d_50[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_45 (Activation)     (None, 288, 288, 64  0           ['batch_normalization_41[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)             (None, 288, 288, 1)  577         ['activation_45[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, 144, 144, 1)  4609        ['activation_33[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)             (None, 72, 72, 1)    4609        ['activation_30[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)             (None, 36, 36, 1)    4609        ['activation_27[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)             (None, 18, 18, 1)    4609        ['activation_24[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)             (None, 9, 9, 1)      4609        ['activation_21[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)             (None, 9, 9, 1)      4609        ['activation_18[0][0]']          \n",
      "                                                                                                  \n",
      " add_6 (Add)                    (None, 288, 288, 1)  0           ['resizing_5[0][0]',             \n",
      "                                                                  'conv2d_51[0][0]']              \n",
      "                                                                                                  \n",
      " resizing_6 (Resizing)          (None, 288, 288, 1)  0           ['conv2d_35[0][0]']              \n",
      "                                                                                                  \n",
      " resizing_7 (Resizing)          (None, 288, 288, 1)  0           ['conv2d_36[0][0]']              \n",
      "                                                                                                  \n",
      " resizing_8 (Resizing)          (None, 288, 288, 1)  0           ['conv2d_37[0][0]']              \n",
      "                                                                                                  \n",
      " resizing_9 (Resizing)          (None, 288, 288, 1)  0           ['conv2d_38[0][0]']              \n",
      "                                                                                                  \n",
      " resizing_10 (Resizing)         (None, 288, 288, 1)  0           ['conv2d_39[0][0]']              \n",
      "                                                                                                  \n",
      " resizing_11 (Resizing)         (None, 288, 288, 1)  0           ['conv2d_40[0][0]']              \n",
      "                                                                                                  \n",
      " activation_46 (Activation)     (None, 288, 288, 1)  0           ['add_6[0][0]']                  \n",
      "                                                                                                  \n",
      " activation_47 (Activation)     (None, 288, 288, 1)  0           ['resizing_5[0][0]']             \n",
      "                                                                                                  \n",
      " activation_48 (Activation)     (None, 288, 288, 1)  0           ['resizing_6[0][0]']             \n",
      "                                                                                                  \n",
      " activation_49 (Activation)     (None, 288, 288, 1)  0           ['resizing_7[0][0]']             \n",
      "                                                                                                  \n",
      " activation_50 (Activation)     (None, 288, 288, 1)  0           ['resizing_8[0][0]']             \n",
      "                                                                                                  \n",
      " activation_51 (Activation)     (None, 288, 288, 1)  0           ['resizing_9[0][0]']             \n",
      "                                                                                                  \n",
      " activation_52 (Activation)     (None, 288, 288, 1)  0           ['resizing_10[0][0]']            \n",
      "                                                                                                  \n",
      " activation_53 (Activation)     (None, 288, 288, 1)  0           ['resizing_11[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 108,886,792\n",
      "Trainable params: 108,834,952\n",
      "Non-trainable params: 51,840\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "class BasnetLoss(keras.losses.Loss):\n",
    "    \"\"\"BASNet hybrid loss.\"\"\"\n",
    "\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__(name=\"basnet_loss\", **kwargs)\n",
    "        self.smooth = 1.0e-9\n",
    "\n",
    "        # Binary Cross Entropy loss.\n",
    "        self.cross_entropy_loss = keras.losses.BinaryCrossentropy()\n",
    "        # Structural Similarity Index value.\n",
    "        self.ssim_value = tf.image.ssim\n",
    "        #  Jaccard / IoU loss.\n",
    "        self.iou_value = self.calculate_iou\n",
    "\n",
    "    def calculate_iou(\n",
    "        self,\n",
    "        y_true,\n",
    "        y_pred,\n",
    "    ):\n",
    "        \"\"\"Calculate intersection over union (IoU) between images.\"\"\"\n",
    "        intersection = backend.sum(backend.abs(y_true * y_pred), axis=[1, 2, 3])\n",
    "        union = backend.sum(y_true, [1, 2, 3]) + backend.sum(y_pred, [1, 2, 3])\n",
    "        union = union - intersection\n",
    "        return backend.mean(\n",
    "            (intersection + self.smooth) / (union + self.smooth), axis=0\n",
    "        )\n",
    "\n",
    "    def call(self, y_true, y_pred):\n",
    "        cross_entropy_loss = self.cross_entropy_loss(y_true, y_pred)\n",
    "\n",
    "        ssim_value = self.ssim_value(y_true, y_pred, max_val=1)\n",
    "        ssim_loss = backend.mean(1 - ssim_value + self.smooth, axis=0)\n",
    "\n",
    "        iou_value = self.iou_value(y_true, y_pred)\n",
    "        iou_loss = 1 - iou_value\n",
    "\n",
    "        # Add all three losses.\n",
    "        return cross_entropy_loss + ssim_loss + iou_loss\n",
    "\n",
    "\n",
    "basnet_model = basnet(\n",
    "    input_shape=[IMAGE_SIZE, IMAGE_SIZE, 3], out_classes=OUT_CLASSES\n",
    ")  # Create model.\n",
    "basnet_model.summary()  # Show model summary.\n",
    "\n",
    "optimizer = keras.optimizers.Adam(learning_rate=1e-4, epsilon=1e-8)\n",
    "# Compile model.\n",
    "basnet_model.compile(\n",
    "    loss=BasnetLoss(),\n",
    "    optimizer=optimizer,\n",
    "    metrics=[keras.metrics.MeanAbsoluteError(name=\"mae\")],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cQnE93WrGBgr"
   },
   "source": [
    "#### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "EoynpOKJGBgs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 1405s 43s/step - loss: 16.0968 - activation_46_loss: 1.9875 - activation_47_loss: 2.0213 - activation_48_loss: 2.0424 - activation_49_loss: 2.1157 - activation_50_loss: 2.0821 - activation_51_loss: 1.9536 - activation_52_loss: 1.9281 - activation_53_loss: 1.9660 - activation_46_mae: 0.2455 - activation_47_mae: 0.2579 - activation_48_mae: 0.2762 - activation_49_mae: 0.2982 - activation_50_mae: 0.2919 - activation_51_mae: 0.2488 - activation_52_mae: 0.2316 - activation_53_mae: 0.2272 - val_loss: 28.6270 - val_activation_46_loss: 6.6966 - val_activation_47_loss: 7.8865 - val_activation_48_loss: 2.6089 - val_activation_49_loss: 2.3249 - val_activation_50_loss: 2.3181 - val_activation_51_loss: 2.3536 - val_activation_52_loss: 2.2359 - val_activation_53_loss: 2.2025 - val_activation_46_mae: 0.7320 - val_activation_47_mae: 0.7401 - val_activation_48_mae: 0.5185 - val_activation_49_mae: 0.2412 - val_activation_50_mae: 0.2624 - val_activation_51_mae: 0.2084 - val_activation_52_mae: 0.2068 - val_activation_53_mae: 0.2444\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd4806e6b80>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basnet_model.fit(train_dataset, validation_data=val_dataset, epochs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CkBwEvnSGBgs"
   },
   "source": [
    "#### 可视化预测\n",
    "基于训练成本过高的原因，在教程里，我们不完全训练 BASNet，所以我们将通过下载[此处](https://github.com/hamidriasat/BASNet/tree/basnet_keras)已经训练好的模型权重放在当前文件夹之下 **（./）** 进行预测效果的演示。\n",
    "\n",
    "您既可以通过手动下载模型权重，然后放置到对应的文件夹，也可以通过运行下方单元格下载模型权重。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "IFJUO6OUGBgs"
   },
   "outputs": [],
   "source": [
    "# !!gdown 1OWKouuAQ7XpXZbWA3mmxDPrFGW71Axrg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "GDKhznR0GBgs"
   },
   "outputs": [],
   "source": [
    "def normalize_output(prediction):\n",
    "    max_value = np.max(prediction)\n",
    "    min_value = np.min(prediction)\n",
    "    return (prediction - min_value) / (max_value - min_value)\n",
    "\n",
    "\n",
    "# Load weights.\n",
    "basnet_model.load_weights(\"./basnet_weights.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TJ0CEsRXGBgs"
   },
   "source": [
    "#### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "pfTEA3rbGBgt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 18s 18s/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for image, mask in val_dataset.take(1):\n",
    "    pred_mask = basnet_model.predict(image)\n",
    "    display([image[0], mask[0], normalize_output(pred_mask[0][0])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 联邦学习模式\n",
    "\n",
    "### 小节引言\n",
    "通过单机模式，我们已经学会到，如何在单机模式下使用 BASNet 实现图像分割，本小节我们将看到如何将单机模型如何快速和低成本地迁移到 SecretFlow 隐语的联邦学习框架之下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 隐语环境初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The version of SecretFlow: 1.2.0.dev20231009\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-14 08:26:29,626\tINFO worker.py:1538 -- Started a local Ray instance.\n"
     ]
    }
   ],
   "source": [
    "import secretflow as sf\n",
    "\n",
    "# Check the version of your SecretFlow\n",
    "print('The version of SecretFlow: {}'.format(sf.__version__))\n",
    "\n",
    "# In case you have a running secretflow runtime already.\n",
    "sf.shutdown()\n",
    "sf.init(['alice', 'bob', 'charlie'], address=\"local\", log_to_driver=False)\n",
    "alice, bob, charlie = sf.PYU('alice'), sf.PYU('bob'), sf.PYU('charlie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进行数据划分\n",
    "\n",
    "首先为了模拟联邦学习多方参与的场景设定，我们先人为进行一下数据集划分。为方便演示，我们对数据按参与方进行均匀划分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们假定联邦学习的数据拥有方是 **alice** 和 **bob**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "from os.path import join\n",
    "\n",
    "dataset_path = './data'\n",
    "partys = ['alice', 'bob']\n",
    "\n",
    "for p in partys:\n",
    "    party_images_path = join(dataset_path, p, 'DUTS-TE-Image')\n",
    "    party_masks_path = join(dataset_path, p, 'DUTS-TE-Mask')\n",
    "\n",
    "    os.makedirs(party_images_path, exist_ok=True)\n",
    "    os.makedirs(party_masks_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以看到，图像分割的训练数据集主要由两部分组成：图片（image）和对应的标注（mask），分别位于 **\"./DUTS-TE/DUTS-TE-Image\"** 和 **\"./DUTS-TE/DUTS-TE-Mask\"** ；我们的参与方数据文件夹分别为 **\"./data/alice/\"** 和 **\"./data/bob/\"**；所以我们分别在参与方数据文件夹下建立 **'DUTS-TE-Image'** 和 **'DUTS-TE-Mask'** 来保存对应的数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_path = join(DATA_DIR, 'DUTS-TE-Image')\n",
    "masks_path = join(DATA_DIR, 'DUTS-TE-Mask')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 0\n",
    "partys_len = len(partys)\n",
    "\n",
    "for image_name in sorted(os.listdir(images_path)):\n",
    "    name, ext = os.path.splitext(image_name)\n",
    "\n",
    "    image_path = join(images_path, image_name)\n",
    "    mask_path = join(masks_path, name + '.png')\n",
    "\n",
    "    if (not os.path.exists(image_path)) or (not os.path.exists(mask_path)):\n",
    "        continue\n",
    "\n",
    "    party_id = index % partys_len\n",
    "\n",
    "    target_images_path = join(dataset_path, partys[party_id], 'DUTS-TE-Image')\n",
    "    target_masks_path = join(dataset_path, partys[party_id], 'DUTS-TE-Mask')\n",
    "\n",
    "    shutil.copy(image_path, target_images_path)\n",
    "    # if you want to watch the process of copying the image to each party, uncomment the following line\n",
    "    # print(f'{index}: copy {image_path}-->{target_images_path}')\n",
    "\n",
    "    shutil.copy(mask_path, target_masks_path)\n",
    "    # if you want to watch the process of copying the image to each party, uncomment the following line\n",
    "    # print(f'{index}: copy {mask_path}-->{target_masks_path}')\n",
    "\n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 封装 DataBuilder\n",
    "\n",
    "在迁移过程，对于数据集的预处理方式，联邦学习模式和单机模式是一样的，我们不再重复。为了完成迁移适配过程，我们只需要参考[在 SecretFlow 中使用自定义 DataBuilder（TensorFlow）](https://github.com/secretflow/secretflow/blob/main/docs/tutorial/CustomDataLoaderTF.ipynb) 封装我们自定义 DataBuilder 即可。现在，参考原教程，我们封装对应的DataBuilder，所以我们也不需要额外写很多代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset_builder(\n",
    "    batch_size=32,\n",
    "):\n",
    "    def dataset_builder(folder_path, stage=\"train\"):\n",
    "        import math\n",
    "\n",
    "        TRAIN_SPLIT_RATIO = 0.90\n",
    "\n",
    "        train_paths, val_paths = load_paths(folder_path, TRAIN_SPLIT_RATIO)\n",
    "\n",
    "        train_dataset = load_dataset(\n",
    "            train_paths[0],\n",
    "            train_paths[1],\n",
    "            IMAGE_SIZE,\n",
    "            OUT_CLASSES,\n",
    "            BATCH_SIZE,\n",
    "            shuffle=True,\n",
    "        )\n",
    "        eval_dataset = load_dataset(\n",
    "            val_paths[0],\n",
    "            val_paths[1],\n",
    "            IMAGE_SIZE,\n",
    "            OUT_CLASSES,\n",
    "            BATCH_SIZE,\n",
    "            shuffle=False,\n",
    "        )\n",
    "\n",
    "        if stage == \"train\":\n",
    "            train_step_per_epoch = len(train_dataset)\n",
    "            return train_dataset, train_step_per_epoch\n",
    "        elif stage == \"eval\":\n",
    "            eval_step_per_epoch = len(eval_dataset)\n",
    "            return eval_dataset, eval_step_per_epoch\n",
    "\n",
    "    return dataset_builder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建 dataset_builder_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_builder_dict = {\n",
    "    alice: create_dataset_builder(\n",
    "        batch_size=32,\n",
    "    ),\n",
    "    bob: create_dataset_builder(\n",
    "        batch_size=32,\n",
    "    ),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义网络结构\n",
    "\n",
    "得益于隐语优异的设计，我们只需要将单机模式下定义的网络结构，进行适当的封装即可，并且可以复用单机模式下定义的大量函数和模块，所以我们只需要少量的代码就可以完成这个过程。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_fl_basnet_model(input_shape, out_classes, name='basnet_model'):\n",
    "    def create_model():\n",
    "        from tensorflow import keras\n",
    "\n",
    "        # Create model\n",
    "        basnet_model = basnet(input_shape=input_shape, out_classes=out_classes)\n",
    "        # Compile model\n",
    "        optimizer = keras.optimizers.Adam(learning_rate=1e-4, epsilon=1e-8)\n",
    "        basnet_model.compile(\n",
    "            loss=BasnetLoss(),\n",
    "            optimizer=optimizer,\n",
    "            metrics=[keras.metrics.MeanAbsoluteError(name=\"mae\")],\n",
    "        )\n",
    "        return basnet_model\n",
    "\n",
    "    return create_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from secretflow.ml.nn import FLModel\n",
    "from secretflow.security.aggregation import SecureAggregator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义TensorFlow 后端的 FLModel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Create proxy actor <class 'secretflow.security.aggregation.secure_aggregator._Masker'> with party alice.\n",
      "INFO:root:Create proxy actor <class 'secretflow.security.aggregation.secure_aggregator._Masker'> with party bob.\n",
      "INFO:root:Create proxy actor <class 'secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_w.PYUFedAvgW'> with party alice.\n",
      "INFO:root:Create proxy actor <class 'secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_w.PYUFedAvgW'> with party bob.\n"
     ]
    }
   ],
   "source": [
    "device_list = [alice, bob]\n",
    "aggregator = SecureAggregator(charlie, [alice, bob])\n",
    "\n",
    "# prepare model\n",
    "input_shape = [IMAGE_SIZE, IMAGE_SIZE, 3]\n",
    "out_classes = OUT_CLASSES\n",
    "\n",
    "# keras model\n",
    "model = create_fl_basnet_model(input_shape, out_classes, name='basnet_model')\n",
    "\n",
    "\n",
    "fed_model = FLModel(\n",
    "    device_list=device_list,\n",
    "    model=model,\n",
    "    aggregator=aggregator,\n",
    "    backend=\"tensorflow\",\n",
    "    strategy=\"fed_avg_w\",\n",
    "    random_seed=2022,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 给出参与方数据集路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    alice: join(dataset_path, partys[0]),\n",
    "    bob: join(dataset_path, partys[1]),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 开始训练模型\n",
    "同样出于演示的目的，我们只简单训练几个 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:FL Train Params: {'x': {PYURuntime(alice): './data/alice', PYURuntime(bob): './data/bob'}, 'y': None, 'batch_size': 32, 'batch_sampling_rate': None, 'epochs': 5, 'verbose': 1, 'callbacks': None, 'validation_data': {PYURuntime(alice): './data/alice', PYURuntime(bob): './data/bob'}, 'shuffle': False, 'class_weight': None, 'sample_weight': None, 'validation_freq': 1, 'aggregate_freq': 1, 'label_decoder': None, 'max_batch_size': 20000, 'prefetch_buffer_size': None, 'sampler_method': 'batch', 'random_seed': 2022, 'dp_spent_step_freq': 1, 'audit_log_dir': None, 'dataset_builder': {PYURuntime(alice): <function create_dataset_builder.<locals>.dataset_builder at 0x7fd260f58d30>, PYURuntime(bob): <function create_dataset_builder.<locals>.dataset_builder at 0x7fd260f58ee0>}, 'wait_steps': 100, 'self': <secretflow.ml.nn.fl.fl_model.FLModel object at 0x7fd53daf0610>}\n",
      "100%|██████████| 32/32 [44:42<00:00, 83.82s/it, epoch: 1/5 -  loss:16.00857162475586  activation_46_loss:2.0647761821746826  activation_47_loss:2.0324795246124268  activation_48_loss:2.0778236389160156  activation_49_loss:2.0445878505706787  activation_50_loss:2.062119245529175  activation_51_loss:1.873093605041504  activation_52_loss:1.8673261404037476  activation_53_loss:1.98636794090271  activation_46_mae:0.27865731716156006  activation_47_mae:0.2687371075153351  activation_48_mae:0.29536810517311096  activation_49_mae:0.2804737687110901  activation_50_mae:0.2785409688949585  activation_51_mae:0.22719474136829376  activation_52_mae:0.22455286979675293  activation_53_mae:0.25536850094795227  val_loss:20.368085861206055  val_activation_46_loss:3.1646745204925537  val_activation_47_loss:3.336146831512451  val_activation_48_loss:2.5135746002197266  val_activation_49_loss:2.2933757305145264  val_activation_50_loss:2.267702579498291  val_activation_51_loss:2.27314829826355  val_activation_52_loss:2.275585651397705  val_activation_53_loss:2.24387788772583  val_activation_46_mae:0.6746677160263062  val_activation_47_mae:0.6996849179267883  val_activation_48_mae:0.47660350799560547  val_activation_49_mae:0.3147360682487488  val_activation_50_mae:0.20802132785320282  val_activation_51_mae:0.17045436799526215  val_activation_52_mae:0.14768938720226288  val_activation_53_mae:0.2697073519229889 ]\n",
      "100%|██████████| 32/32 [44:20<00:00, 83.15s/it, epoch: 2/5 -  loss:13.461732864379883  activation_46_loss:1.7250224351882935  activation_47_loss:1.7619400024414062  activation_48_loss:1.6791943311691284  activation_49_loss:1.6646625995635986  activation_50_loss:1.6584991216659546  activation_51_loss:1.647373080253601  activation_52_loss:1.6555969715118408  activation_53_loss:1.669443130493164  activation_46_mae:0.22743959724903107  activation_47_mae:0.230095773935318  activation_48_mae:0.20803558826446533  activation_49_mae:0.19053444266319275  activation_50_mae:0.17684155702590942  activation_51_mae:0.1663295328617096  activation_52_mae:0.16224732995033264  activation_53_mae:0.17607778310775757  val_loss:17.092275619506836  val_activation_46_loss:2.267822027206421  val_activation_47_loss:2.236685276031494  val_activation_48_loss:2.2907803058624268  val_activation_49_loss:2.2660715579986572  val_activation_50_loss:1.929986596107483  val_activation_51_loss:2.0072691440582275  val_activation_52_loss:1.9896384477615356  val_activation_53_loss:2.10402250289917  val_activation_46_mae:0.1431700587272644  val_activation_47_mae:0.1427074819803238  val_activation_48_mae:0.1505149006843567  val_activation_49_mae:0.16673283278942108  val_activation_50_mae:0.11402619630098343  val_activation_51_mae:0.11548315733671188  val_activation_52_mae:0.1181015893816948  val_activation_53_mae:0.1553722769021988 ]\n",
      "100%|██████████| 32/32 [44:54<00:00, 84.21s/it, epoch: 3/5 -  loss:12.266992568969727  activation_46_loss:1.5260404348373413  activation_47_loss:1.5344090461730957  activation_48_loss:1.5332894325256348  activation_49_loss:1.5361995697021484  activation_50_loss:1.503815770149231  activation_51_loss:1.5211528539657593  activation_52_loss:1.5511409044265747  activation_53_loss:1.5609447956085205  activation_46_mae:0.1633487194776535  activation_47_mae:0.16452531516551971  activation_48_mae:0.16328425705432892  activation_49_mae:0.16432426869869232  activation_50_mae:0.15655435621738434  activation_51_mae:0.15474833548069  activation_52_mae:0.1552736461162567  activation_53_mae:0.15696871280670166  val_loss:17.203031539916992  val_activation_46_loss:2.3510024547576904  val_activation_47_loss:2.351886510848999  val_activation_48_loss:2.2449982166290283  val_activation_49_loss:2.2234275341033936  val_activation_50_loss:1.9592950344085693  val_activation_51_loss:2.10693097114563  val_activation_52_loss:1.91500723361969  val_activation_53_loss:2.0504839420318604  val_activation_46_mae:0.2923806309700012  val_activation_47_mae:0.30491212010383606  val_activation_48_mae:0.2015693038702011  val_activation_49_mae:0.259004145860672  val_activation_50_mae:0.10972847789525986  val_activation_51_mae:0.11618858575820923  val_activation_52_mae:0.11013747751712799  val_activation_53_mae:0.12166451662778854 ]\n",
      "100%|██████████| 32/32 [44:54<00:00, 84.21s/it, epoch: 4/5 -  loss:11.887752532958984  activation_46_loss:1.4831573963165283  activation_47_loss:1.496115803718567  activation_48_loss:1.4822880029678345  activation_49_loss:1.4847438335418701  activation_50_loss:1.4645109176635742  activation_51_loss:1.4843111038208008  activation_52_loss:1.4909228086471558  activation_53_loss:1.501702904701233  activation_46_mae:0.16722702980041504  activation_47_mae:0.16943305730819702  activation_48_mae:0.15856586396694183  activation_49_mae:0.16392359137535095  activation_50_mae:0.14746731519699097  activation_51_mae:0.14393015205860138  activation_52_mae:0.1427759826183319  activation_53_mae:0.14263062179088593  val_loss:16.53447151184082  val_activation_46_loss:2.189110279083252  val_activation_47_loss:2.4149715900421143  val_activation_48_loss:1.925722360610962  val_activation_49_loss:1.8521143198013306  val_activation_50_loss:2.5779011249542236  val_activation_51_loss:1.970674753189087  val_activation_52_loss:1.7697486877441406  val_activation_53_loss:1.8342268466949463  val_activation_46_mae:0.10972431302070618  val_activation_47_mae:0.10948605090379715  val_activation_48_mae:0.11358214914798737  val_activation_49_mae:0.11093635112047195  val_activation_50_mae:0.10677933692932129  val_activation_51_mae:0.10744746029376984  val_activation_52_mae:0.11080104112625122  val_activation_53_mae:0.11139702051877975 ]\n",
      "100%|██████████| 32/32 [44:55<00:00, 84.25s/it, epoch: 5/5 -  loss:11.421000480651855  activation_46_loss:1.4359709024429321  activation_47_loss:1.458670973777771  activation_48_loss:1.407894253730774  activation_49_loss:1.3943818807601929  activation_50_loss:1.473480224609375  activation_51_loss:1.4193472862243652  activation_52_loss:1.4211714267730713  activation_53_loss:1.4100836515426636  activation_46_mae:0.13962911069393158  activation_47_mae:0.14179329574108124  activation_48_mae:0.14112454652786255  activation_49_mae:0.1405549943447113  activation_50_mae:0.13879072666168213  activation_51_mae:0.13845589756965637  activation_52_mae:0.13873867690563202  activation_53_mae:0.1355820745229721  val_loss:16.218276977539062  val_activation_46_loss:1.8988500833511353  val_activation_47_loss:1.944711446762085  val_activation_48_loss:1.9846420288085938  val_activation_49_loss:1.828316330909729  val_activation_50_loss:2.318599224090576  val_activation_51_loss:1.9814397096633911  val_activation_52_loss:2.1665217876434326  val_activation_53_loss:2.0951969623565674  val_activation_46_mae:0.1109127327799797  val_activation_47_mae:0.11216922849416733  val_activation_48_mae:0.11376891285181046  val_activation_49_mae:0.10986456274986267  val_activation_50_mae:0.10743771493434906  val_activation_51_mae:0.10770834237337112  val_activation_52_mae:0.10720323026180267  val_activation_53_mae:0.10760793089866638 ]\n"
     ]
    }
   ],
   "source": [
    "history = fed_model.fit(\n",
    "    data,\n",
    "    None,\n",
    "    validation_data=data,\n",
    "    epochs=5,\n",
    "    batch_size=32,\n",
    "    aggregate_freq=1,\n",
    "    sampler_method=\"batch\",\n",
    "    random_seed=2022,\n",
    "    dp_spent_step_freq=1,\n",
    "    dataset_builder=data_builder_dict,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化训练历史"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'activation_46_loss', 'activation_47_loss', 'activation_48_loss', 'activation_49_loss', 'activation_50_loss', 'activation_51_loss', 'activation_52_loss', 'activation_53_loss', 'activation_46_mae', 'activation_47_mae', 'activation_48_mae', 'activation_49_mae', 'activation_50_mae', 'activation_51_mae', 'activation_52_mae', 'activation_53_mae', 'val_loss', 'val_activation_46_loss', 'val_activation_47_loss', 'val_activation_48_loss', 'val_activation_49_loss', 'val_activation_50_loss', 'val_activation_51_loss', 'val_activation_52_loss', 'val_activation_53_loss', 'val_activation_46_mae', 'val_activation_47_mae', 'val_activation_48_mae', 'val_activation_49_mae', 'val_activation_50_mae', 'val_activation_51_mae', 'val_activation_52_mae', 'val_activation_53_mae'])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.global_history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Draw loss values for training & validation\n",
    "plt.plot(history.global_history['loss'])\n",
    "plt.plot(history.global_history['val_loss'])\n",
    "plt.title('FLModel loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jx2nevDmzZs1ixYoVfPDBB1it1mLXUdhzHz9+PMuXL6dPnz7Url2bo0eP8t5771GjRg3HoP7u3bsTFRVF+/btiYyMZOvWrbzzzjv06dMn1xgjkWIx7wY0EfeV3y3GF6pdu3a+t8sPGTLEMAzn2+Dzk9dt8IZhGLt27TJuuukmIzQ01PD19TVat25tzJkzJ9f++/btM/r162f4+/sbVatWNUaMGGHMmzcvz9vK165da9xwww1GlSpVDKvVatSuXdu45ZZbjMWLF+c6/4vdBn/w4EHj+uuvN0JDQ42QkBDj5ptvNg4fPmwAxpgxY3LVOHDgQKNatWqG1Wo16tatawwbNszIyMhwtPnoo4+MunXrGp6enk61X3gbfI6kpCTDz8/PAIwvv/wy1/b09HTj0UcfNaKjow0/Pz+jffv2xsqVK/M83o8//mg0adLE8PLycrol/sJbwQ3D/jiC//3vf0ZMTIzh7e1tNGjQwHj11VedHi9gGPbb4IcNG5arrtq1axuDBg3K8++0sAr6bzQ5OdkYMWKEERUVZfj4+BiXXXZZnn8/F1Pcc1+8eLHRv39/IyYmxvDx8TFiYmKM22+/3fj3338dbT744AOjQ4cOjv8O69WrZzz++ONGYmJikesUyYvFMErxEaAiIiIibkBjgERERKTS0RggEREXlZ2dfdHB54GBgUV+PMHFnDx5kszMzHy3e3p6luhuMRFXoEtgIiIuau/evXkOtD7fmDFjSv15UZ06dWLZsmX5bq9du7bjeUMi7srUHqDJkyczefJkxw9S06ZNGT16dL63iX700Ud8/vnnjueFtGzZkhdffNFpgsbBgwfz2WefOe3Xo0ePYs+uLCJilqioqItOr3LhHWul4bXXXivwgYjnT3Ei4q5M7QGaPXs2np6eNGjQAMMw+Oyzz3j11VdZu3YtTZs2zdX+zjvvpH379rRr1w5fX19efvllZs6cyebNm6levTpgD0BHjhxhypQpjv2sVithYWHldl4iIiLi2lzuElh4eDivvvoqQ4YMuWjb7OxswsLCHM84AXsASkhIYNasWWVcqYiIiLgrlxkEnZ2dzYwZM0hNTaVt27aF2ictLY2srCzCw8Od1i9dupSIiAjCwsLo0qULzz//PFWqVMn3OBkZGY6nj4L9qagnT56kSpUqxZqJWkRERMqfYRgkJycTExNz0YmBTX8Q4oYNG4yAgADD09PTCAkJMX7++edC7/vAAw8YdevWNU6fPu1Y9/XXXxs//vijsWHDBmPmzJlG48aNjVatWhlnzpzJ9zhjxozJ92F1eumll1566aWXe70OHDhw0Qxh+iWwzMxM9u/fT2JiIt999x0ff/wxy5Ytc8xjlJ+XXnqJV155haVLl9KsWbN82+3evZt69eqxaNEiunbtmmebC3uAEhMTqVWrFgcOHCA4OLh4JyYiIiLlKikpiZo1a5KQkEBISEiBbU2/BObj40P9+vUB+11df/31F2+++SYffPBBvvtMnDiRl156iUWLFhUYfsB+h0TVqlXZuXNnvgHIarXmOf9NcHCwApCIiIibKczwFdMD0IVsNptTb8yFXnnlFV544QXmz5/vNMFffg4ePMiJEyeIjo4uzTJFRETEjZkagEaNGkWvXr2oVasWycnJTJs2jaVLlzJ//nwABg4cSPXq1ZkwYQIAL7/8MqNHj2batGnUqVOH+Ph44NyTUFNSUhg3bhw33ngjUVFR7Nq1iyeeeIL69evTo0cP085TREREXIupAejo0aMMHDiQuLg4QkJCaNasGfPnz+faa68FYP/+/U6juCdPnkxmZiY33XST03FynoTq6enJhg0b+Oyzz0hISCAmJobu3bvz3HPP5XmJS0RERCon0wdBu6KkpCRCQkJITEwscAxQdnY2WVlZ5VhZxeLt7Y2np6fZZYiISAVR2N/f4IJjgNyBYRjEx8eTkJBgdiluLzQ0lKioKD1vSUREypUCUDHkhJ+IiAj8/f31y7sYDMMgLS2No0ePAmiQuoiIlCsFoCLKzs52hJ+Cni4tF5czoeLRo0eJiIjQ5TARESk3F3lOtFwoZ8yPv7+/yZVUDDl/jxpLJSIi5UkBqJh02at06O9RRETMoAAkIiIilY4CkBRbnTp1mDRpktlliIiIFJkCUCVgsVgKfI0dO7ZYx/3rr78YOnRo6RYrIiJSDnQXWHnLSAZvf/Aovzue4uLiHMvffPMNo0ePZvv27Y51gYGBjmXDMMjOzsbL6+L/aVSrVq10CxURESkn6gEqT0mH4MROSI4v14+NiopyvEJCQrBYLI7327ZtIygoiLlz59KyZUusVisrVqxg165d9O/fn8jISAIDA2nVqhWLFi1yOu6Fl8AsFgsff/wx119/Pf7+/jRo0ICffvqpXM9VRESkMBSASoFhGKRlnrn4Cz/SsmykJcSTlpJUuH0KeJXmLCZPPvkkL730Elu3bqVZs2akpKTQu3dvFi9ezNq1a+nZsyd9+/Zl//79BR5n3Lhx3HLLLWzYsIHevXtz5513cvLkyVKrU0REpDToElgpOJ2VTZPR84u4V8l7gbaM74G/T+l8hePHj3dMQgsQHh5O8+bNHe+fe+45Zs6cyU8//cTw4cPzPc7gwYO5/fbbAXjxxRd56623+PPPP+nZs2ep1CkiIlIa1AMkAFx55ZVO71NSUnjsscdo3LgxoaGhBAYGsnXr1ov2ADVr1syxHBAQQHBwsGO6CxEREVehHqBS4OftyZbxPQq/Q9opSNwPWKBqQ/C2FvtzS0tAQIDT+8cee4yFCxcyceJE6tevj5+fHzfddBOZmZkFHsfb29vpvcViwWazlVqdIiIipUEBqBRYLJaiXYryrgrZSZCRBGmHoGoDcLEnIv/+++8MHjyY66+/HrD3CO3du9fcokREREqJLoGZwWKBkJpg8YCsVEg9bnZFuTRo0IAffviBdevWsX79eu644w715IiISIWhAGQWLx8IjrEvJx+GMxnm1nOB119/nbCwMNq1a0ffvn3p0aMHV1xxhdlliYiIlAqLUZr3UlcQSUlJhISEkJiYSHBwsNO29PR09uzZQ2xsLL6+viX7IMOA4zvsvUDWIAiv53KXwspaqf59iohIpVbQ7+8LqQfITBYLhNYCLPYnRJ8+ZXZFIiIilYICkNm8fSEoyr6ceBCys8ytR0REpBJQAHIFgRHg5QdGtj0EiYiISJlSAHIFFo+zl8KA9AQ4nWhqOSIiIhWdApCr8PG39wQBJB4A2xlz6xEREanAFIBcSWA0eFrBlgVJh82uRkREpMJSAHIlHh4QWtO+nHbCfmeYiIiIlDoFIFdjDQL/KvblhAOgpy+LiIiUOgUgVxQcAx7ekJ0BKXFmVyMiIlLhKAC5Ig8v+1xhAClHITPN3HrO6tSpEyNHjnS8r1OnDpMmTSpwH4vFwqxZs8q0LhERkaJSAHJVfiHgG2pfTtgPRskuhfXt25eePXvmue23337DYrGwYcOGIh3zr7/+YujQoSWqS0RExAwKQK4spAZYPOHMaXtPUAkMGTKEhQsXcvBg7gctTpkyhSuvvJJmzZoV6ZjVqlXD39+/RHWJiIiYQQHIlXl620MQQHI8ZKUX+1DXXXcd1apVY+rUqU7rU1JSmDFjBgMGDOD222+nevXq+Pv7c9lll/H1118XeMwLL4Ht2LGDDh064OvrS5MmTVi4cGGx6xURESlLXmYXUCEYBmSV0TgdTx/7mKCMZDi+3XnGeG//Qs8e7+XlxcCBA5k6dSpPP/00lrP7zZgxg+zsbO666y5mzJjB//3f/xEcHMzPP//M3XffTb169WjduvVFj2+z2bjhhhuIjIxk9erVJCYmOo0XEhERcSUKQKUhKw1ejCn/z33qMPgEFLr5vffey6uvvsqyZcvo1KkTYL/8deONN1K7dm0ee+wxR9uHHnqI+fPn8+233xYqAC1atIht27Yxf/58YmLsfxcvvvgivXr1Kto5iYiIlANdAqtEGjVqRLt27fj0008B2LlzJ7/99htDhgwhOzub5557jssuu4zw8HACAwOZP38++/fvL9Sxt27dSs2aNR3hB6Bt27Zlch4iIiIlpR6g0uDtb++NKUuGASd2QVYqWIMhrI79c4toyJAhPPTQQ7z77rtMmTKFevXq0bFjR15++WXefPNNJk2axGWXXUZAQAAjR44kMzOz9M9FRETEZApApcFiKdKlqGKr1hCObbPPFZadCZbAIh/illtuYcSIEUybNo3PP/+cBx54AIvFwu+//07//v256667APuYnn///ZcmTZoU6riNGzfmwIEDxMXFER0dDcCqVauKXJ+IiEh50CUwd+LtC0FR9uXEg5CdVeRDBAYGcuuttzJq1Cji4uIYPHgwAA0aNGDhwoX88ccfbN26lf/+978cOXKk0Mft1q0bl1xyCYMGDWL9+vX89ttvPP3000WuT0REpDwoALmbwAjw8gMjGxIPFesQQ4YM4dSpU/To0cMxZueZZ57hiiuuoEePHnTq1ImoqCgGDBhQ6GN6eHgwc+ZMTp8+TevWrbnvvvt44YUXilWfiIhIWbMYhmGY9eGTJ09m8uTJ7N27F4CmTZsyevToAu8cmjFjBs8++yx79+6lQYMGvPzyy/Tu3dux3TAMxowZw0cffURCQgLt27dn8uTJNGjQoNB1JSUlERISQmJiIsHBwU7b0tPT2bNnD7Gxsfj6+hbthEtLZioc/9e+HF4XfEPMqaMUuMTfp4iIVAgF/f6+kKk9QDVq1OCll15izZo1/P3333Tp0oX+/fuzefPmPNv/8ccf3H777QwZMoS1a9cyYMAABgwYwKZNmxxtXnnlFd566y3ef/99Vq9eTUBAAD169CA9vfgPEXQ5PgEQEGFfTjgAtmxz6xEREXEzpvYA5SU8PJxXX32VIUOG5Np26623kpqaypw5cxzrrrrqKlq0aMH777+PYRjExMTw6KOPOp5pk5iYSGRkJFOnTuW2224rVA0u3wME9tBzbJt9MLR/VQitaV4tJeAyf58iIuL23KYH6HzZ2dlMnz6d1NTUfJ8fs3LlSrp16+a0rkePHqxcuRKAPXv2EB8f79QmJCSENm3aONrkJSMjg6SkJKeXy/PwhNBa9uW045CRYm49IiIibsT0ALRx40YCAwOxWq3cf//9zJw5M99br+Pj44mMjHRaFxkZSXx8vGN7zrr82uRlwoQJhISEOF41a7pJb4o1CPyr2JcT9oOtZDPGi4iIVBamB6CGDRuybt06Vq9ezQMPPMCgQYPYsmVLudYwatQoEhMTHa8DBw5cdB+XuXIYHAMe3pCdASn5hzxX5TJ/jyIiUqmYHoB8fHyoX78+LVu2ZMKECTRv3pw333wzz7ZRUVG5nk1z5MgRoqKiHNtz1uXXJi9Wq5Xg4GCnV368vb0BSEsro8lPi8rD69yM8SlHINNF6iqknL/HnL9XERGR8uByT4K22WxkZGTkua1t27YsXrzYaZbxhQsXOsYMxcbGEhUVxeLFi2nRogVgHxCV07tUGjw9PQkNDeXo0aMA+Pv7O2ZWN43FFzyCIDMJju2F8DpgMT3bFsgwDNLS0jh69CihoaF4enqaXZKIiFQipgagUaNG0atXL2rVqkVycjLTpk1j6dKlzJ8/H4CBAwdSvXp1JkyYAMCIESPo2LEjr732Gn369GH69On8/ffffPjhhwBYLBZGjhzJ888/T4MGDYiNjeXZZ58lJiamSA/1u5ic3qScEOQSbNmQfAIMGxxNBt+CR7+7itDQ0AJ750RERMqCqQHo6NGjDBw4kLi4OEJCQmjWrBnz58/n2muvBWD//v14eJzryWjXrh3Tpk3jmWee4amnnqJBgwbMmjWLSy+91NHmiSeeIDU1laFDh5KQkMDVV1/NvHnzSvUWa4vFQnR0NBEREWRlFX06ijKzdRssHgsePnD71xBW2+yKCuTt7a2eHxERMYXLPQfIFRTlOQIuxTDgyxtg1xKo1Q4G/wwern0pTEREpLS45XOApBRYLHDdJPAOgP1/wJopZlckIiLikhSAKpqw2tB1tH154ZhiT5gqIiJSkSkAVUSt/wM1WkNmMsz5n/3SmIiIiDgoAFVEHp7Q723w9IEd82HT92ZXJCIi4lIUgCqqiEbQ4XH78twnIPWEufWIiIi4EAWgiqz9SIhoAmknYN6TZlcjIiLiMhSAKjIvH+j3jv2p0Bu/hX/nm12RiIiIS1AAquhqtISrHrQvz3kE0pPMrUdERMQFKABVBp2fgrA6kHQQFo8zuxoRERHTKQBVBj4B0PdN+/JfH8O+P8ytR0RExGQKQJVF3U5w+d325Z8egqx0U8sRERExkwJQZdL9eQiMghM7YfkrZlcjIiJiGgWgysQvFPpMtC+vmARxG8ysRkRExDQKQJVN477QpD8Y2fDTcMg+Y3ZFIiIi5U4BqDLq9Sr4hkLcelj5jtnViIiIlDsFoMooKBJ6vGhfXjoBTuwytx4REZFypgBUWbW4A+p2hjPp8NPDYLOZXZGIiEi5UQCqrCwW6DsJvP1h3wr4Z6rZFYmIiJQbBaDKLKwOdHnWvrxwDCQeMrUcERGR8qIAVNm1+S9UvxIykuDnR8EwzK5IRESkzCkAVXYentD/HfDwhn/nwuYfzK5IRESkzCkACUQ0hg6P2Zd/eQLSTppbj4iISBlTABK7qx+BiCaQdhzmjTK7GhERkTKlACR2Xj7Q723AAhumw46FZlckIiJSZhSA5JwaV8JVD9qXZ4+EjGRTyxERESkrCkDirMvTEFobkg7C4vFmVyMiIlImFIDEmU8A9H3TvvznR7B/lbn1iIiIlAEFIMmtXmdocRdgwE8PQVa62RWJiIiUKgUgyVuP5yEwEo7/C8tfNbsaERGRUqUAJHnzC4PeZ4PP75MgfqOp5YiIiJQmBSDJX5P+0Lgv2M7Aj8Mh+4zZFYmIiJQKBSApWO+J4BsCcetg1XtmVyMiIlIqFICkYEFR0P0F+/KvL8CJXebWIyIiUgoUgOTiLr8LYjvCmXSYPUIzxouIiNtTAJKLs1jszwby9oe9v8E/n5ldkYiISIkoAEnhhMdCl2fsywuehaTD5tYjIiJSAgpAUnht7ofqLSEjCX5+VJfCRETEbZkagCZMmECrVq0ICgoiIiKCAQMGsH379gL36dSpExaLJderT58+jjaDBw/Otb1nz55lfToVn4cn9HsHPLxh+y+weabZFYmIiBSLqQFo2bJlDBs2jFWrVrFw4UKysrLo3r07qamp+e7zww8/EBcX53ht2rQJT09Pbr75Zqd2PXv2dGr39ddfl/XpVA6RTeCaR+zLc5+AtJPm1iMiIlIMXmZ++Lx585zeT506lYiICNasWUOHDh3y3Cc8PNzp/fTp0/H3988VgKxWK1FRUaVbsNhd8yhs+RGObYP5T8H175tdkYiISJG41BigxMREIHfIKcgnn3zCbbfdRkBAgNP6pUuXEhERQcOGDXnggQc4ceJEqdZaqXlZ7ZfCsMD6r2HnIrMrEhERKRKLYbjGSFabzUa/fv1ISEhgxYoVhdrnzz//pE2bNqxevZrWrVs71uf0CsXGxrJr1y6eeuopAgMDWblyJZ6enrmOk5GRQUZGhuN9UlISNWvWJDExkeDg4JKfXEU190lYPRlCasKDq8AaaHZFIiJSiSUlJRESElKo398uE4AeeOAB5s6dy4oVK6hRo0ah9vnvf//LypUr2bBhQ4Htdu/eTb169Vi0aBFdu3bNtX3s2LGMGzcu13oFoIvISIHJbSFhv/0OsV4vm12RiIhUYkUJQC5xCWz48OHMmTOHX3/9tdDhJzU1lenTpzNkyJCLtq1bty5Vq1Zl586deW4fNWoUiYmJjteBAweKVH+lZQ2E6ybZl1d/AAf+NLUcERGRwjI1ABmGwfDhw5k5cyZLliwhNja20PvOmDGDjIwM7rrrrou2PXjwICdOnCA6OjrP7VarleDgYKeXFFL9rtDiTsCwzxh/JuOiu4iIiJjN1AA0bNgwvvzyS6ZNm0ZQUBDx8fHEx8dz+vRpR5uBAwcyatSoXPt+8sknDBgwgCpVqjitT0lJ4fHHH2fVqlXs3buXxYsX079/f+rXr0+PHj3K/Jwqpe7PQ0AEHN8OyyeaXY2IiMhFmRqAJk+eTGJiIp06dSI6Otrx+uabbxxt9u/fT1xcnNN+27dvZ8WKFXle/vL09GTDhg3069ePSy65hCFDhtCyZUt+++03rFZrmZ9TpeQfDr1ftS+veB3iN5lbj4iIyEW4zCBoV1KUQVRylmHAN3fBtjkQczkMWQSepj5mSkREKhm3GwQtFYDFAr0ngjUEDq+13x4vIiLiohSApPQER0OP5+3LS16Ak7vNrUdERCQfCkBSui6/G2I7wJnTMHuEZowXERGXpAAkpctigb5vgpcf7FkOa78wuyIREZFcFICk9IXXhS5P25fnPwNJcQW3FxERKWcKQFI22jwAMVdARiL88pguhYmIiEtRAJKy4ekF/d4GDy/7rfFbfjS7IhEREQcFICk7UZfC1Y/Yl395DNJOmluPiIjIWQpAUrY6PAZVG0LqMVjwjNnViIiIAApAUta8rPZLYVhg3Vewc7HZFYmIiCgASTmo1QZaD7UvzxkJGSmmliMiIqIAJOWj62gIqQkJ+2HJ82ZXIyIilZwCkJQPayD0nWRfXv0+HPjL1HJERKRyUwCS8lO/GzS/HTDgp+FwJsPsikREpJJSAJLy1eNFCKgGx7bBb6+bXY2IiFRSCkBSvvzDodcr9uXfXoMjW8ytR0REKiUFICl/Ta+Hhn3AlmW/FGbLNrsiERGpZBSApPxZLNBnIliD4dAa+6BoERGRcqQAJOYIjoHuz9mXFz8HJ/eYW4+IiFQqCkBinisGQZ1r4MxpmD1CM8aLiEi5UQAS81gs0PdN8PKFPctg7ZdmVyQiIpWEApCYq0o96Py0fXnB05Acb249IiJSKSgAifmuehCiW0B6IvzymNnViIhIJaAAJObz9IL+74CHF2ydDVt+NLsiERGp4BSAxDVEXQbtR9qXf3kcTp8ytRwREanYFIDEdXR4HKpeAilHYMEzZlcjIiIVmAKQuA5vX+j3NmCx3xG261ezKxIRkQpKAUhcS62roPV/7MuzR0Bmqrn1iIhIhaQAJK6n62gIrgEJ+2DJC2ZXIyIiFZACkLgeaxD0nWRfXvUeHPzb1HJERKTiUQAS19TgWmh2K2DAj8PhTKbZFYmISAWiACSuq8cE8K8Kx7bCitfNrkZERCoQBSBxXQFVoPcr9uXlE+HoVnPrERGRCkMBSFxb0xvgkl5gy7JfCrNlm12RiIhUAApA4tosFujzGliD4dDf8OeHZlckIiIVgAKQuL6Q6nDtOPvy4vFwaq+p5YiIiPtTABL3cMVgqH01ZKXB7JFgGGZXJCIibkwBSNyDhwf0ewu8fGH3r7BumtkViYiIGzM1AE2YMIFWrVoRFBREREQEAwYMYPv27QXuM3XqVCwWi9PL19fXqY1hGIwePZro6Gj8/Pzo1q0bO3bsKMtTkfJQpR50GmVfnv8UJB8xtx4REXFbpgagZcuWMWzYMFatWsXChQvJysqie/fupKYWPP9TcHAwcXFxjte+ffuctr/yyiu89dZbvP/++6xevZqAgAB69OhBenp6WZ6OlIe2wyG6OaQnwNzHza5GRETclJeZHz5v3jyn91OnTiUiIoI1a9bQoUOHfPezWCxERUXluc0wDCZNmsQzzzxD//79Afj888+JjIxk1qxZ3HbbbaV3AlL+PL2g3zvwYSfY8iNsnQ2N+5pdlYiIuBmXGgOUmJgIQHh4eIHtUlJSqF27NjVr1qR///5s3rzZsW3Pnj3Ex8fTrVs3x7qQkBDatGnDypUr8zxeRkYGSUlJTi9xYdHN4OqR9uWfH4XTp0wtR0RE3I/LBCCbzcbIkSNp3749l156ab7tGjZsyKeffsqPP/7Il19+ic1mo127dhw8eBCA+Ph4ACIjI532i4yMdGy70IQJEwgJCXG8atasWUpnJWWmwxNQpQGkHIEFz5pdjYiIuBmXCUDDhg1j06ZNTJ8+vcB2bdu2ZeDAgbRo0YKOHTvyww8/UK1aNT744INif/aoUaNITEx0vA4cOFDsY0k58faFfm/bl9d+AbuXmlqOiIi4F5cIQMOHD2fOnDn8+uuv1KhRo0j7ent7c/nll7Nz504Ax9igI0ec7xA6cuRIvuOGrFYrwcHBTi9xA7XbQqv77MuzR0Bmmrn1iIiI2zA1ABmGwfDhw5k5cyZLliwhNja2yMfIzs5m48aNREdHAxAbG0tUVBSLFy92tElKSmL16tW0bdu21GoXF9F1DATXsD8d+tcXzK5GRETchKkBaNiwYXz55ZdMmzaNoKAg4uPjiY+P5/Tp0442AwcOZNSoUY7348ePZ8GCBezevZt//vmHu+66i3379nHfffaeAIvFwsiRI3n++ef56aef2LhxIwMHDiQmJoYBAwaU9ylKWfMNhuvesC+veg8OrTG3HhERcQum3gY/efJkADp16uS0fsqUKQwePBiA/fv34+FxLqedOnWK//znP8THxxMWFkbLli35448/aNKkiaPNE088QWpqKkOHDiUhIYGrr76aefPm5XpgolQQl3SHy26Bjd/Cjw/B0KXg5WN2VSIi4sIshqFJlS6UlJRESEgIiYmJGg/kLlJPwLutIO0EdH4aOj5hdkUiIlLOivL72yUGQYuUWEAV6PWKfXn5q3B0m7n1iIiIS1MAkorj0huhQQ/IzoSfHgJbttkViYiIi1IAkorDYoHrXgefIDj4J/z5kdkViYiIi1IAkoolpAZcO86+vHg8nNpXcHsREamUFICk4ml5D9RuD1mpMGckaJy/iIhcQAFIKh4PD+j7FnhaYdcSWF/w9CoiIlL5KABJxVS1PnR60r4870lIOWpuPSIi4lIUgKTiavcQRDWD9ASYq+cCiYjIOQpAUnF5ekP/d8DiCZtnwrafza5IRERchAKQVGzRzaH9w/blnx+F0wmmliMiIq5BAUgqvo7/B+H1IDkOFo42uxoREXEBCkBS8Xn7Qb+37cv/fAZ7lptbj4iImE4BSCqHOu3hyiH25Z8ehsw0c+sRERFTKQBJ5dFtLARXh1N7YOmLZlcjIiImUgCSysM3GK57w7688l049I+59YiIiGkUgKRyuaQHXHoTGDb7jPHZWWZXJCIiJlAAksqn18vgFw5HNsHvk8yuRkRETKAAJJVPQFV7CAJY9goc225uPSIiUu4UgKRyuuxmaNAdsjPtl8JsNrMrEhGRcqQAJJWTxQJ9XgefQDiwGv762OyKRESkHBUrAB04cICDBw863v/555+MHDmSDz/8sNQKEylzoTXtt8YDLB4HCftNLUdERMqPV3F2uuOOOxg6dCh333038fHxXHvttTRt2pSvvvqK+Ph4Ro/WdAPiJq4cApu+h/0rYc7/4M7v7L1D4v4ykuHkbjixC07ughO77e9P7rY/HbxuJ6jfDep2BN8Qs6sVkXJmMQzDKOpOYWFhrFq1ioYNG/LWW2/xzTff8Pvvv7NgwQLuv/9+du/eXRa1lpukpCRCQkJITEwkODjY7HKkrB3fAZPbQ3YGXP8BNL/N7IqksDJSzoaaXWeDzu5zoSf1aOGOYfGEmq2hXleo3xWiW4CHRgeIuKOi/P4uVg9QVlYWVqsVgEWLFtGvXz8AGjVqRFxcXHEOKWKeqg2g0//B4vEw70n7L8LAamZXJTkyUuxP787pyTm5+2xvzi5IOVLwvv5VoUo9CK9rnxC3Sl37cupx2LkYdi6CEzvsPYD7V8Kvz4N/FajXxf7fQb0uEBRZPucpIuWqWAGoadOmvP/++/Tp04eFCxfy3HPPAXD48GGqVKlSqgWKlIt2D8PmmRC/EeY+ATdPMbuiyiUzFU7uybsnJyW+4H39q9jDTXjd88LO2ZdfaP77NbjW/uepfbBrsT0Q7VkOaSdg4wz7CyDqMvulsnpdoWYb8PIplVMWEXMV6xLY0qVLuf7660lKSmLQoEF8+umnADz11FNs27aNH374odQLLU+6BFZJHV4HH3UBIxtu+xoa9Ta7ooolMy3/npzki/Qc+4Vf0JNTD8Jj7csFhZyiys6Cg3+d6x2KW+e83ScQYjvYe4bqd7PXICIuoyi/v4sVgACys7NJSkoiLCzMsW7v3r34+/sTERFRnEO6DAWgSmzhaPj9TQiKhmGrNTi2qLJO59+Tk3y44H39wi7oyclZrmvfZoaUY7D7V3sg2rUYUo85bw+ve653qM7VYA00p04RAcohAJ0+fRrDMPD39wdg3759zJw5k8aNG9OjR4/iVe1CFIAqsazTMLmd/Zd2y3ug7ySzK3I9Wel59OSc/TPpUMH7+oac14NTz/mSlX94+dRfXDYbHNlo7xnauQQOrALbmXPbPX2g1lXnAlFkU91RKFLOyjwAde/enRtuuIH777+fhIQEGjVqhLe3N8ePH+f111/ngQceKHbxrkABqJLbuwKm9rEvD/7Z/i/7yiYrHU7tvaAn5+yt5EmHgAL+t2ENOTvYuN4FPTn1XD/kFEV6Euz97WwgWgwJ+5y3B0bZ7yqr18X+qkjnLuKiyjwAVa1alWXLltG0aVM+/vhj3n77bdauXcv333/P6NGj2bp1a7GLdwUKQMLskbBmiv0X9wN/2J8bU9GcybCHnLx6chIPUnDICXa+VHX++Bz/8MrX82EY9r+3nDC09zfISjuvgQWqX3Gud6h6S/As1j0oIlKAMr8NPi0tjaCgIAAWLFjADTfcgIeHB1dddRX79u27yN4ibuDacfDvPPsvtaUT4NrxZldUPGcy8+/JSTxAgSHHJ6iAnpwqlS/kFMRisf+9VKkHbf5rD5f7V567XHZ0MxxaY38te9l+KbBup3PPHgqpYfYZiFQ6xQpA9evXZ9asWVx//fXMnz+f//3vfwAcPXpUPSZSMfiGwHVvwNe3wR/vQNPrIeZys6vK25lM++WXXD05u+w9OUYBE736BOYx6PjsckBVhZzi8rLaA07dTtAdSDoMu5bYA9GuXyE9Abb8aH8BVGt0tneoC9RuD96+5tUuUkkU6xLYd999xx133EF2djZdunRh4cKFAEyYMIHly5czd+7cUi+0POkSmDh8d699qozIy2Dor+DpbU4d2Vn259VcGHBO7LL35Fw05MTmvlRVpR4EVFPIKW+2bDj0z9lnDy2y9wqd//15+UGd9ucul1VtoO9IpJDK5Tb4+Ph44uLiaN68OR5nHxv/559/EhwcTKNGjYpzSJehACQOKcfg3VZw+hR0eRY6PFZ2n5WdZZ+Q1elS1dmgk3DA/nyi/HgHnLtl/MKenMAI/QJ1ZWknYc+yc5fLLnxcQEgtqH/2uUOxHfRoBpEClEsAypEzK3yNGhXnGrYCkDhZ/w3MHAqeVrh/BVS7pPjHyj5jv1x1/vNxcoJOwv6LhBz/c7eMX9iTExipkFMRGAYc3Xqud2jfH5CdeW57zrxl9bvae4c0b5mIkzIPQDabjeeff57XXnuNlJQUAIKCgnj00Ud5+umnHT1C7koBSJwYBnx1M+xcCLXawuBfCv6lk30GEvefN/v4+T05+52fHXMhL7/8e3KCohRyKpvMVNj7+7mpOk7scN7uXxXqdT43fijQvR9CK1JSZR6ARo0axSeffMK4ceNo3749ACtWrGDs2LH85z//4YUXXihe5S5CAUhySdgP77WFzBToPRGuvNe+7uQu+5OPnXpy9l0k5Pjm35MTFK2QI/k7f96y3csgM9l5e1Qze+9Q/W5Qo7XmLZNKp8wDUExMDO+//75jFvgcP/74Iw8++CCHDl3kabBnTZgwgR9++IFt27bh5+dHu3btePnll2nYsGG++3z00Ud8/vnnbNq0CYCWLVvy4osv0rp1a0ebwYMH89lnnznt16NHD+bNm1eouhSAJE+rP4S5j4OHF2ABW1b+bb18ISzW+UnHjp6caF22kJLLzoIDf567XBa33nm7TyDEdrSPH6rXVfOWSaVQ5gHI19eXDRs2cMklzmMhtm/fTosWLTh9+nShjtOzZ09uu+02WrVqxZkzZ3jqqafYtGkTW7ZsISAgIM997rzzTtq3b0+7du3w9fXl5ZdfZubMmWzevJnq1asD9gB05MgRpkw5N6O31Wp1mresIApAkiebzf6E6P1/2N97Ws+7u6ruBT05MQo5Ur5Sjtlvtc/pIUo77rw9vN653qE6V4NP3v+PFXFnZR6A2rRpQ5s2bXjrrbec1j/00EP8+eefrF69uqiHBODYsWNERESwbNkyOnToUKh9srOzCQsL45133mHgwIGAPQAlJCQwa9asYtWhACT5ykiB+I32B9cFV1fIEddks0H8hnNh6MDqPOYta3suEEU00aVXqRDK/EnQr7zyCn369GHRokW0bdsWgJUrV3LgwAF++eWX4hwSgMTERADCwws/Z05aWhpZWVm59lm6dCkRERGEhYXRpUsXnn/+eapUqVLs2kQA+2zftduaXYVIwTw8IKaF/XXNo/Z5y/YsP3e5LGG//db7Pctg4Wj7Zdl6Xe2Xy+p21rxlUikU+zb4w4cP8+6777Jt2zYAGjduzNChQ3n++ef58MMPi3w8m81Gv379SEhIYMWKFYXe78EHH2T+/Pls3rwZX1/701OnT5+Ov78/sbGx7Nq1i6eeeorAwEBWrlyJp6dnrmNkZGSQkZHheJ+UlETNmjXVAyQiFY9h2Afr71xkD0R7foMz5w9bsNjnKsvpHYq5QvOWidso1+cAnW/9+vVcccUVZGcX8CyTfDzwwAPMnTuXFStWFPqZQi+99BKvvPIKS5cupVmzZvm22717N/Xq1WPRokV07do11/axY8cybty4XOsVgESkwstKPzdv2a4lcHSL83bfEHuvUM6zh0Kqm1OnSCG4XQAaPnw4P/74I8uXLyc2tnB3KkycOJHnn3+eRYsWceWVV160fbVq1Xj++ef573//m2ubeoBERM5KPHRu3rLdS+3zlp2vWuOzvUNdoVY7zVsmLqXMxwCVFsMweOihh5g5cyZLly4tdPh55ZVXeOGFF5g/f36hws/Bgwc5ceIE0dHReW63Wq1YrdYi1S4iUiGFVIcr7ra/cuYty7lcdmgNHNtqf6185+y8ZVefu1xWpb4GU4vbMLUH6MEHH2TatGn8+OOPTs/+CQkJwc/PD4CBAwdSvXp1JkyYAMDLL7/M6NGjmTZtmuMhjACBgYEEBgaSkpLCuHHjuPHGG4mKimLXrl088cQTJCcns3HjxkIFHd0FJiKSh7ST9l6hnYvtgSg5znl7SK1zvUOxHcFX//+U8lVml8BuuOGGArcnJCSwbNmyQgcgSz7/UpgyZQqDBw8GoFOnTtSpU4epU6cCUKdOHfbt25drnzFjxjB27FhOnz7NgAEDWLt2LQkJCcTExNC9e3eee+45IiMjC1WXApCIyEUYhn280M6zd5btX+k8b5mHl/1p1DmBKKq5HhshZa7MAtA999xTqHbnP4DQHSkAiYgUUWYq7F1xLhCd3OW83b+qfb6y+l01b5mUGdMGQVcUCkAiIiV0au/ZMLTY/ryhzBTn7VHN7OOG6neFmm3A09uUMqViUQAqIQUgEZFSdCYTDv55rncofoPzdp8giO1w7nJZWB1TyhT3pwBUQgpAIiJlKOXo2Vvtzw6mTjvhvL1K/bNPpu6qecukSBSASkgBSESknNhsEL/e3jO0c4l93jLjvBtpHPOWnb1cpnnLpAAKQCWkACQiYpL0RPu8ZTnjhxL3O2/Pmbes+a1Q5xqFIXGiAFRCCkAiIi7AMODEzrO9Q4vtd5mdP29Z7fbQ6UkFIXFQACohBSAREReUlQ77/4AtP8G6r849d0hBSM5SACohBSAREReXeAhWvAH/fKYgJA5F+f2tx3KKiIj7CakOfSbCw+ug1X/sg6X3/Q6f9YWpfezjiEQKoAAkIiLuq6AgNKUP7PnN7ArFRSkAiYiI+8szCK2Az65TEJI8KQCJiEjF4QhCa6HVfQpCki8FIBERqXhCakCf1xSEJF8KQCIiUnEVFISmXqcgVIkpAImISMWXVxDa+9u5ILR3hdkVSjlTABIRkcojvyA0tY+CUCWjACQiIpXP+UHoyiHg4a0gVMkoAImISOUVUgOuex1GrFMQqmQUgERERHKCUL49Qr+bXaGUMgUgERGRHKE18wlCvRWEKhgFIBERkQs5BaF7FYQqIAUgERGR/ITWhOveyDsIfdZXQciNKQCJiIhcTF5BaM/yc0Fo3x9mVyhFpAAkIiJSWPkFoSm9FITcjAKQiIhIUSkIuT0FIBERkeJyBKF/oOU9FwShfgpCLkwBSEREpKRCa0HfSRcEoWXnBaGVZlcoF1AAEhERKS35BqGeCkIuRgGoHKVlnuH1hf+SnJ5ldikiIlKWFIRcngJQOXrv1128tXgHnScuY8bfB7DZDLNLEhGRsuQUhAaDh9e5IPR5fwUhEykAlaNWseHUrRrA8ZQMHv9uA9dP/oN1BxLMLktERMpaaC3o+6b9rrGcILR7qYKQiSyGYagb4gJJSUmEhISQmJhIcHBwqR4784yNKb/v4a3FO0jNzAbg5pY1eKJnI6oFWUv1s0RExEWd2gcrXoe1X4LtjH1d3U7QaRTUusrU0txZUX5/KwDloSwDUI6jSem8NG8bP/xzCIAgqxcjujVgYNs6+HipY05EpFJQECpVCkAlVB4BKMeafacY+9NmNh5KBKBetQDG9G1Kh0uqlennioiICzm1D357DdZ9pSBUAgpAJVSeAQjAZjOYseYAr8zbzonUTACubRLJs32aUKuKf5l/voiIuIg8g1Bn6PSkglAhKACVUHkHoByJp7N4a/EOpv6xl2ybgY+XB0OvqcuDnevh7+NVbnWIiIjJ8g1Co6BWG3Nrc2EKQCVkVgDKseNIMuNmb2HFzuMARAX78lSfxvRtFo3FYin3ekRExCQKQkWiAFRCZgcgAMMwWLDlCM/N2cLBU6cBaF0nnDH9mtA0JsSUmkRExCQKQoVSlN/fpt5uNGHCBFq1akVQUBAREREMGDCA7du3X3S/GTNm0KhRI3x9fbnsssv45ZdfnLYbhsHo0aOJjo7Gz8+Pbt26sWPHjrI6jTJhsVjo0TSKRY905NFrL8HX24M/956k79sreGbWRk6dHSskIiKVQFht6PcWPLQGrhh49jlCv8Kn3eGL62H/arMrdDumBqBly5YxbNgwVq1axcKFC8nKyqJ79+6kpqbmu88ff/zB7bffzpAhQ1i7di0DBgxgwIABbNq0ydHmlVde4a233uL9999n9erVBAQE0KNHD9LT08vjtEqVr7cnD3VtwOJHO3Fds2hsBny5aj+dJi7l85V7OZNtM7tEEREpL2F1oN/bzkFo15JzQejAn2ZX6DZc6hLYsWPHiIiIYNmyZXTo0CHPNrfeeiupqanMmTPHse6qq66iRYsWvP/++xiGQUxMDI8++iiPPfYYAImJiURGRjJ16lRuu+22i9bhCpfA8rNq9wnG/rSZbfHJADSKCmJM36a0rVfF5MpERKTcndp79tLYtHOXxup1sV8aq9na1NLM4DaXwC6UmGh/Fk54eHi+bVauXEm3bt2c1vXo0YOVK+2PEd+zZw/x8fFObUJCQmjTpo2jzYUyMjJISkpyermqq+pWYc5DV/Nc/6aE+HmzLT6Z2z9axbBp/3Ao4bTZ5YmISHnKr0fok2vVI3QRLhOAbDYbI0eOpH379lx66aX5touPjycyMtJpXWRkJPHx8Y7tOevya3OhCRMmEBIS4njVrFmzJKdS5rw8Pbi7bR2WPtaJu66qhYcFft4QR9fXlvLW4h2kZ2WbXaKIiJSnnCA0/G+4/O4LgtANCkJ5cJkANGzYMDZt2sT06dPL/bNHjRpFYmKi43XgwIFyr6E4wgJ8eH7AZcx+6Gpa1wknPcvG6wv/pdvry5i3KR4XuropIiLlITwW+r9zLghZPGHXYgWhPLhEABo+fDhz5szh119/pUaNGgW2jYqK4siRI07rjhw5QlRUlGN7zrr82lzIarUSHBzs9HInTWNC+Oa/V/HW7ZcTFezLwVOnuf/LNdz9yZ/sOJJsdnkiIlLecoLQQ2sUhPJhagAyDIPhw4czc+ZMlixZQmxs7EX3adu2LYsXL3Zat3DhQtq2bQtAbGwsUVFRTm2SkpJYvXq1o01FZLFY6Nc8hiWPdWR45/r4eHqwYudxer75G+NnbyHxdJbZJYqISHm7aBD6y+wKTWPqXWAPPvgg06ZN48cff6Rhw4aO9SEhIfj5+QEwcOBAqlevzoQJEwD7bfAdO3bkpZdeok+fPkyfPp0XX3yRf/75xzF26OWXX+all17is88+IzY2lmeffZYNGzawZcsWfH19L1qXK98FVlj7TqTy/M9bWbjF3hNWJcCHJ3o25OaWNfHw0NOkRUQqpZN74LeJsO5rMM6OF63fDTo+CTVbmVtbKXCbJ0HnN63DlClTGDx4MACdOnWiTp06TJ061bF9xowZPPPMM+zdu5cGDRrwyiuv0Lt3b8d2wzAYM2YMH374IQkJCVx99dW89957XHLJJYWqqyIEoBzL/j3GuNmb2X3M/mylZjVCGNO3KS1rh5lcmYiImKaCBiG3CUCuqiIFIIDMMzY+X7mXSYt2kJJhf07EDVdU58mejYgIvniPmIiIVFAVLAgpAJVQRQtAOY4mp/PqvO3MWHMQgAAfTx7u2oB72sfi4+US4+FFRMQM+QWhTqOgxpXm1lYECkAlVFEDUI51BxIY89Nm1h9IAKBu1QCe7duEzg0jzC1MRETMlWcQuhY6PekWQUgBqIQqegACsNkMvv/nIC/P287xlAwAujaK4NnrmlCnaoDJ1YmIiKlO7oblr8F69wpCCkAlVBkCUI6k9CzeXryDKb/v5YzNwMfTgyHXxDK8c30CrF5mlyciImZysyCkAFRClSkA5dh5NIXxc7aw/N9jAEQGWxnVqzH9W8Tke7eeiIhUEvkGoVFQo6W5tZ1HAaiEKmMAAvvjAxZvPcr4OVvYfzINgJa1wxjXrymXVg8xuToRETFdXkGoQXf7XWMuEIQUgEqosgagHOlZ2XyyYg/vLNnJ6axsLBa4rVUtHut+CVUCrWaXJyIiZjuxC357DdZPd6kgpABUQpU9AOWISzzNS3O38eO6wwAE+3rxyLWXcNdVtfHy1G3zIiKVnosFIQWgElIAcvbnnpOM/WkzW+KSALgkMpCxfZvSrn5VkysTERGX4CJBSAGohBSAcsu2GUz/az8T52/nVJp9YtVel0bxVO/G1Az3N7k6ERFxCXkGoR7Q6f+getkHIQWgElIAyl9CWiZvLPyXL1btw2aA1cuD+zvW4/6O9fDz8TS7PBERcQUndsHyibDhm3INQgpAJaQAdHFb45IYN3szq3afBKB6qB9P92lMr0ujdNu8iIjYlXMQUgAqIQWgwjEMg182xvPCz1s4nJgOQNu6VRjbrykNo4JMrk5ERFyGIwhNB8NmX9fqP9BnYql+TFF+f+tWHik2i8VCn2bRLH60Ew93bYCPlwcrd5+g91u/MfanzSSeHSskIiKVXJV6cP1kGP43NL8DLB5Q/QpTS1IPUB7UA1Q8B06m8cLPW5m3OR6AMH9vHu/RiFtb1cTTQ5fFRETkrBO7ILQ2eJbulEu6BFZCCkAls2LHccbN3syOoykANI0JZly/plxZJ9zkykREpCJTACohBaCSy8q28cXKfbyx6F+S088A0L9FDKN6NSYqxNfk6kREpCJSACohBaDSczwlg4nzt/PN3wcwDPD38WRY5/rcd00sVi/dNi8iIqVHAaiEFIBK34aDCYz9aTP/7E8AoHYVf57t04SujSN027yIiJQKBaASUgAqGzabwax1h5gwdxvHkjMA6HhJNUb3bUK9aoEmVyciIu5OAaiEFIDKVkrGGd5esoNPV+whK9vAy8PCvVfH8lCX+gT5eptdnoiIuCkFoBJSACofu4+l8NycLfy6/RgAVQOtPNmrETdcXh0P3TYvIiJFpABUQgpA5WvJtiOMn72FvSfSAGhRM5Rx/ZrSvGaouYWJiIhbUQAqIQWg8pdxJpspv+/l7cU7SM20zxdzy5U1eLxHI6oFWU2uTkRE3IECUAkpAJnnSFI6L8/dxg9rDwEQZPViRLcGDGpXB29PzdwiIiL5UwAqIQUg863Zd5IxP21m06EkAOpHBDKmbxOuaVDN5MpERMRVKQCVkAKQa8i2Gcz4+wCvzN/OydRMALo3ieSZPk2oVcXf5OpERMTVKACVkAKQa0lMy2LS4n/5fOU+sm0GPl4e/LdDXR7oVA9/n9KdSE9ERNyXAlAJKQC5pn+PJDP2p838sesEANEhvjzVuzHXNYvW06RFREQBqKQUgFyXYRjM3xzPc3O2cijhNACtY8MZ27cpTWL0XYmIVGYKQCWkAOT60rOy+WDZbiYv20l6lg0PC9zZpjaPXHsJYQE+ZpcnIiImUAAqIQUg93Eo4TQv/rKVnzfEARDq782j3RtyR+taeOpp0iIilYoCUAkpALmflbtOMG72ZrbFJwPQODqYsX2b0KZuFZMrExGR8qIAVEIKQO7pTLaNaX/u57UF/5J4OguAvs1jGNWrETGhfiZXJyIiZU0BqIQUgNzbydRMXluwnWl/7scwwM/bkwc71eM/Heri6+1pdnkiIlJGFIBKSAGoYth0KJFxszfz195TANQM9+OZPk3o3iRSt82LiFRACkAlpABUcRiGwU/rDzPhl23EJ6UDcE2Dqozp24T6EUEmVyciIqVJAaiEFIAqntSMM7y3dCcfLd9DZrYNLw8Lg9rVYUS3BgT7eptdnoiIlIKi/P42dXrt5cuX07dvX2JiYrBYLMyaNavA9oMHD8ZiseR6NW3a1NFm7NixubY3atSojM9EXF2A1YvHezRi4SMd6NY4kjM2g09W7KHLxKV8+9cBbDb9O0BEpDIxNQClpqbSvHlz3n333UK1f/PNN4mLi3O8Dhw4QHh4ODfffLNTu6ZNmzq1W7FiRVmUL26odpUAPh50JVPvaUXdagEcT8nkie83cP17v/PP/lNmlyciIuXE1Jkke/XqRa9evQrdPiQkhJCQEMf7WbNmcerUKe655x6ndl5eXkRFRZVanVLxdGoYQbt6Vfnsj728uXgH6w8mcsN7f3DjFTX4v14NiQjyNbtEEREpQ6b2AJXUJ598Qrdu3ahdu7bT+h07dhATE0PdunW588472b9/f4HHycjIICkpyeklFZ+Plwf/6VCXJY915KaWNQD4/p+DdJm4jA+X7yLzjM3kCkVEpKy4bQA6fPgwc+fO5b777nNa36ZNG6ZOncq8efOYPHkye/bs4ZprriE5OTnfY02YMMHRuxQSEkLNmjXLunxxIRFBvky8uTkzH2xH8xohpGSc4cVfttFz0nKWbj9qdnkiIlIGXOYuMIvFwsyZMxkwYECh2k+YMIHXXnuNw4cP4+OT/+SXCQkJ1K5dm9dff50hQ4bk2SYjI4OMjAzH+6SkJGrWrKm7wCohm83gu38O8sq8bRxPyQSgW+MInunThDpVA0yuTkREClKUu8BMHQNUXIZh8Omnn3L33XcXGH4AQkNDueSSS9i5c2e+baxWK1artbTLFDfk4WHhlitr0vPSKN5atIOpf+xl0dajLP/3OPddE8uwzvUJsLrlj42IiJzHLS+BLVu2jJ07d+bbo3O+lJQUdu3aRXR0dDlUJhVFsK83z1zXhHkjr+GaBlXJzLbx3tJddHltKTPXHtT4IBERN2dqAEpJSWHdunWsW7cOgD179rBu3TrHoOVRo0YxcODAXPt98skntGnThksvvTTXtscee4xly5axd+9e/vjjD66//no8PT25/fbby/RcpGKqHxHE5/e25sO7W1Iz3I8jSRn875v1tHphEf/33QZW7DjOmWyFIRERd2NqX/7ff/9N586dHe8feeQRAAYNGsTUqVOJi4vLdQdXYmIi33//PW+++Waexzx48CC33347J06coFq1alx99dWsWrWKatWqld2JSIVmsVjo3jSKDpdU4+PfdvPZyn0cS87gm78P8M3fB6ga6EPvy6Lp2zyGlrXC8PDQPGMiIq7OZQZBuxJNhSEFybYZrN5zgtnr45i3KY5TaVmObdEhvvQ5G4aa1QjRpKsiIuVIc4GVkAKQFFZWto3fdx5n9vo4FmyOJznjjGNbrXB/+jaP5rpmMTSKClIYEhEpYwpAJaQAJMWRnpXNsn+PMXv9YRZvPcrprGzHtvoRgfRtFkPf5tHUrRZoYpUiIhWXAlAJKQBJSaVlnmHx1qPMXn+YpduPkXneQOmmMcH0bR5Dn8uiqRnub2KVIiIViwJQCSkASWlKSs9iweYjzF5/mBU7j5N93szzl9cKpW+zGPo0iyYyWPOPiYiUhAJQCSkASVk5mZrJvE3xzF5/mFV7TpDz02exQJvYcPo2j6HXpdGEBxT8gE8REclNAaiEFICkPBxNSufnjXHMXn+Yf/YnONZ7eli4un5VrmsWTfemUYT4eZtXpIiIG1EAKiEFIClvB0+l8fOGOGZvOMymQ0mO9T6eHnRsWI2+zWPo1jgCfx9NwyEikh8FoBJSABIz7T6WwpwNcfy0/jA7j6Y41vt5e9KlcQR9m8XQqWE1fL09TaxSRMT1KACVkAKQuALDMNh+JJk56+09Q/tOpDm2BVq96N40kr7NY7i6flW8Pd1yWj8RkVKlAFRCCkDiagzDYOOhRGavP8ycDXHEJaY7toX6e9Pr0ij6NouhTd0qeGoqDhGppBSASkgBSFyZzWawZv8p5qw/zM8b4ziekunYVi3IenYqjmgur6l5yUSkclEAKiEFIHEXZ7JtrN5zktnrDzN3UzyJp8/NS1Y91I8+zaLp2yyGS6sHayoOEanwFIBKSAFI3FHmmZx5yQ6zYMsRUs6bl6xOFX/6No+hb/MYLokMMrFKEZGyowBUQgpA4u7Ss7JZuv0os9fHsXjbEdKzzk3F0TAyiOuaRXNd8xhiqwaYWKWISOlSACohBSCpSFIzzrBo6xFmr49j2b9Hyco+9yN/WfUQ+jaPpk+zGKqH+plYpYhIySkAlZACkFRUiWlZzN9in4rjj10nnOYla1k7jL7NoundLJqIIM1LJiLuRwGohBSApDI4kZLB3LPzkv2596RjXjIPC1xVtwp9m8fQs2kUYZqXTETchAJQCSkASWUTn3huXrJ1BxIc6708LFzToCrXNYuhe9NIgnw1L5mIuC4FoBJSAJLK7MDJNOZssIehLXHnzUvm5UHns/OSdW0UiZ+PpuIQEdeiAFRCCkAidjuPpjBnw2Fmrz/MrmOpjvX+Pp50bRxJ32bRdGxYDauXwpCImE8BqIQUgEScGYbB1rhkexjacJgDJ087tgX5etGjaRR9m8fQrl4VzUsmIqZRACohBSCR/BmGwfqDOfOSHeZIUoZjW3iADz3PzkvWOjZc85KJSLlSACohBSCRwrHZDP7ed4rZ6w/zy8Y4TqSem5csIshqn4qjeQyX1wzVVBwiUuYUgEpIAUik6M5k21i5+wSz1x9m3qZ4ktLPTcVRPdSPvs1juK5ZNE1jNC+ZiJQNBaASUgASKZnMMzZ+23HMMS9ZWma2Y1vdqgFc1zyGfs2jqR+heclEpPQoAJWQApBI6Tmdmc2v248ye/1hlmw7SsaZc/OSNYoKcvQM1a6ieclEpGQUgEpIAUikbKRknGHRliPMXn+Y5TuOOc1L1rxGCH2bx9CnWTTRIZqXTESKTgGohBSARMpeQlom8zfHM3t9HH/sOs5505LRuk441zWPptel0VQLsppXpIi4FQWgElIAEilfx5IzmLcpjtnr4/hz70nHeg8LtKtXlb7No+nRNIpQf81LJiL5UwAqIQUgEfMcTjjNL2fnJVt/MNGx3tvTQocG1biueTTXNoki0OplYpUi4ooUgEpIAUjENew7keqYl2xbfLJjvdXLgy6NIujbPIYujSLw9dZUHCKiAFRiCkAirmfHkWRmb4hjzvrD7D5+bl6yAB9Prm0SSd/mMVzToBo+XpqKQ6SyUgAqIQUgEddlGAabDyc5eoYOJZyblyzY18s+FUfzGNrWrYKX5iUTqVQUgEpIAUjEPRiGwdoDCcxef5ifN8RxNPncvGRVAnzodZl9XrJWdcLx0LxkIhWeAlAJKQCJuJ9sm8Ffe0865iU7lZbl2BYV7OuYl6x5jRBNxSFSQSkAlZACkIh7y8q28ccu+7xk8zfFk5xxbl6ymuF+tK9XlfAAH8L8fQj19ybM34ewAG9C/e3rQvy8NZO9iBtSACohBSCRiiPjTDbLth9jzoY4Fm45wums7IvvhH08UViAz9lQ5O0clvzPhaVQf297Oz9v/H081bskYiK3CUDLly/n1VdfZc2aNcTFxTFz5kwGDBiQb/ulS5fSuXPnXOvj4uKIiopyvH/33Xd59dVXiY+Pp3nz5rz99tu0bt260HUpAIlUTGmZZ/h12zF2Hk3hVFomCWmZnErLcvx5Ki2T5PNmsS8qHy8PQv288+lZOheacpZD/b0J9fPWYG2RUlKU39+mPkksNTWV5s2bc++993LDDTcUer/t27c7nVhERIRj+ZtvvuGRRx7h/fffp02bNkyaNIkePXqwfft2p3YiUvn4+3jRp1l0gW3OZNtIOH1eKErNJOFsOHKsTz37Pu3cn5nZNjLP2DianOE0GLswgny9LuhZOi8snRegcoJVqL8PAeptEikRl7kEZrFYCt0DdOrUKUJDQ/Ns06ZNG1q1asU777wDgM1mo2bNmjz00EM8+eSThapFPUAiUhSGYZCWme0Uihw9S6lZJJzOa30mSSXpbfL0IMTf2yk02QPSBZfsAs4FqhA/b7zV2yQVmNv0ABVXixYtyMjI4NJLL2Xs2LG0b98egMzMTNasWcOoUaMcbT08POjWrRsrV67M93gZGRlkZJz7F1tSUlLZFS8iFY7FYiHA6kWA1YsaYYXf70y2jcTTWbkuw51/ae78XqacAJV5xkZmto1jyRkcK2pvk9WL0IB8wpLTOKec3iZvAq1e6m2SCsetAlB0dDTvv/8+V155JRkZGXz88cd06tSJ1atXc8UVV3D8+HGys7OJjIx02i8yMpJt27ble9wJEyYwbty4si5fRMSJl6cHVQKtVAks/Iz3hmFwOis79+U5pwB17s+c9Ymn7Y8FSM44Q3LGGQ6cPH2RTzrH29NCiF8eYSkg96DwMH/vsz1TPuptEpfmVgGoYcOGNGzY0PG+Xbt27Nq1izfeeIMvvvii2McdNWoUjzzyiON9UlISNWvWLFGtIiJlwWKx4O/jhb+PF9VD/Qq9X7bNONvblJnnOKZzvVA5wcm+PuOMjaxsg+MpGRxPKVpvU6DVK4+epfMu2V1wl12IvzdB6m2ScuJWASgvrVu3ZsWKFQBUrVoVT09Pjhw54tTmyJEjTneJXchqtWK1Fv5fYCIi7sbTw0J4gA/hAT5F2u/02bFNucJSqnNoyullOpWWSeLpLAwDUjLOkJJxhoOnCt/b5OVhcQz0zm98U6i/j+NcqgTYxzbpSd9SVG4fgNatW0d0tP2uDh8fH1q2bMnixYsdg6ltNhuLFy9m+PDhJlYpIuKe/Hw88fPxI6aIvU1Jpy/sWXIOS+f3QuUEp/QsG2dsBsdTMjmeklnoz/OwQNj5oSjw7HLOukArVc4LTKH+Ppo0V8wNQCkpKezcudPxfs+ePaxbt47w8HBq1arFqFGjOHToEJ9//jkAkyZNIjY2lqZNm5Kens7HH3/MkiVLWLBggeMYjzzyCIMGDeLKK6+kdevWTJo0idTUVO65555yPz8RkcrI08Niv/usiL1N6Vlne5tS8x4U7tQLlZrJiVT7c5tsBpw4+76wgny9HKEoPMAekMLOBiR7aDpvOcAHfx+37y+QC5j6jf79999ODzbMGYczaNAgpk6dSlxcHPv373dsz8zM5NFHH+XQoUP4+/vTrFkzFi1a5HSMW2+9lWPHjjF69Gji4+Np0aIF8+bNyzUwWkREXIuvtyfRIX5EhxS+tynzjI1TaZmcTLW/TqRmcjIlw7F8Ki2TEynntp9Ky8RmQHL6GZLTz7D3RFoha/OgSoDV6dJb2HnL53qerIT7+xDsp7FMrs5lngPkSvQcIBGRiilnMHhOIDqZmnE2NJ0LTCdTnUNTZratyJ/jdbYX7PxepHCnwGR1hKacweF6InjJVfjnAImIiBRHUQeDG4ZBSsYZTqVmcSL1XM/SyQteJ86GqZMpmaRmZnPGZhTpOU0WC4T4eTv1KIVfcInuwiDl6+1Zkr+KSk8BSEREJB8Wi4UgX2+CfL2pVcW/UPvkjGU6vxfJEZBSs87+eS5IJaTZ75rLefzA7mOphfqcAB9Pwh2X3bztQSnQJ9dlupzwpAdaOlMAEhERKUVFHcuUM/+c86U3+6W5Uxf0OOWsO2MzSM3MJvXk6UI/1NLH0yNXL5Kjx+nsoO8w/3NjmUIr+OMFFIBERERM5OXpQdVAK1UDrVCI+3UMwyAp/cy5oJTiHI4uvEx3IjWD9Cz79CnxSenEJ6UXqq7zHy9wfk/SuTvlrI5HDeSMZXKnxwsoAImIiLgRi8VCiJ83IX7exFYNKNQ+pzOznccw5fQ0pZ0bAH7+pbmSPl7AeQB43mOYqgVZTR3HpAAkIiJSwfn5eFLDx58aYYUbx5R5xkZC2rmeJMfjBdLOG8NUwscL3NO+DmP6Ni3pqRWbApCIiIg48fHyICLYl4hg30K1t519vMC5S2/5jGE6LzRVKeKDMkubApCIiIiUiEcRn/5tGAbZNnMfQ6gAJCIiIuXKYrHg5WnuHWbuM1xbREREpJQoAImIiEilowAkIiIilY4CkIiIiFQ6CkAiIiJS6SgAiYiISKWjACQiIiKVjgKQiIiIVDoKQCIiIlLpKACJiIhIpaMAJCIiIpWOApCIiIhUOgpAIiIiUuloNvg8GIYBQFJSksmViIiISGHl/N7O+T1eEAWgPCQnJwNQs2ZNkysRERGRokpOTiYkJKTANhajMDGpkrHZbBw+fJigoCAsFkupHjspKYmaNWty4MABgoODS/XYrkDn5/4q+jnq/NxfRT9HnV/xGYZBcnIyMTExeHgUPMpHPUB58PDwoEaNGmX6GcHBwRXyP+wcOj/3V9HPUefn/ir6Oer8iudiPT85NAhaREREKh0FIBEREal0FIDKmdVqZcyYMVitVrNLKRM6P/dX0c9R5+f+Kvo56vzKhwZBi4iISKWjHiARERGpdBSAREREpNJRABIREZFKRwFIREREKh0FoDLw7rvvUqdOHXx9fWnTpg1//vlnge1nzJhBo0aN8PX15bLLLuOXX34pp0qLpyjnN3XqVCwWi9PL19e3HKstmuXLl9O3b19iYmKwWCzMmjXrovssXbqUK664AqvVSv369Zk6dWqZ11lcRT2/pUuX5vr+LBYL8fHx5VNwEU2YMIFWrVoRFBREREQEAwYMYPv27Rfdz11+Botzfu72Mzh58mSaNWvmeEhe27ZtmTt3boH7uMv3B0U/P3f7/i700ksvYbFYGDlyZIHtzPgOFYBK2TfffMMjjzzCmDFj+Oeff2jevDk9evTg6NGjebb/448/uP322xkyZAhr165lwIABDBgwgE2bNpVz5YVT1PMD+9M+4+LiHK99+/aVY8VFk5qaSvPmzXn33XcL1X7Pnj306dOHzp07s27dOkaOHMl9993H/Pnzy7jS4inq+eXYvn2703cYERFRRhWWzLJlyxg2bBirVq1i4cKFZGVl0b17d1JTU/Pdx51+BotzfuBeP4M1atTgpZdeYs2aNfz999906dKF/v37s3nz5jzbu9P3B0U/P3Cv7+98f/31Fx988AHNmjUrsJ1p36Ehpap169bGsGHDHO+zs7ONmJgYY8KECXm2v+WWW4w+ffo4rWvTpo3x3//+t0zrLK6int+UKVOMkJCQcqqudAHGzJkzC2zzxBNPGE2bNnVad+uttxo9evQow8pKR2HO79dffzUA49SpU+VSU2k7evSoARjLli3Lt427/QyerzDn584/gznCwsKMjz/+OM9t7vz95Sjo/Nz1+0tOTjYaNGhgLFy40OjYsaMxYsSIfNua9R2qB6gUZWZmsmbNGrp16+ZY5+HhQbdu3Vi5cmWe+6xcudKpPUCPHj3ybW+m4pwfQEpKCrVr16ZmzZoX/ZeOu3Gn768kWrRoQXR0NNdeey2///672eUUWmJiIgDh4eH5tnHn77Aw5wfu+zOYnZ3N9OnTSU1NpW3btnm2cefvrzDnB+75/Q0bNow+ffrk+m7yYtZ3qABUio4fP052djaRkZFO6yMjI/MdMxEfH1+k9mYqzvk1bNiQTz/9lB9//JEvv/wSm81Gu3btOHjwYHmUXOby+/6SkpI4ffq0SVWVnujoaN5//32+//57vv/+e2rWrEmnTp34559/zC7tomw2GyNHjqR9+/Zceuml+bZzp5/B8xX2/NzxZ3Djxo0EBgZitVq5//77mTlzJk2aNMmzrTt+f0U5P3f8/qZPn84///zDhAkTCtXerO9Qs8FLmWrbtq3Tv2zatWtH48aN+eCDD3juuedMrEwKo2HDhjRs2NDxvl27duzatYs33niDL774wsTKLm7YsGFs2rSJFStWmF1KmSjs+bnjz2DDhg1Zt24diYmJfPfddwwaNIhly5blGxLcTVHOz92+vwMHDjBixAgWLlzo8oO1FYBKUdWqVfH09OTIkSNO648cOUJUVFSe+0RFRRWpvZmKc34X8vb25vLLL2fnzp1lUWK5y+/7Cw4Oxs/Pz6Sqylbr1q1dPlQMHz6cOXPmsHz5cmrUqFFgW3f6GcxRlPO7kDv8DPr4+FC/fn0AWrZsyV9//cWbb77JBx98kKutO35/RTm/C7n697dmzRqOHj3KFVdc4ViXnZ3N8uXLeeedd8jIyMDT09NpH7O+Q10CK0U+Pj60bNmSxYsXO9bZbDYWL16c7/Xdtm3bOrUHWLhwYYHXg81SnPO7UHZ2Nhs3biQ6OrqsyixX7vT9lZZ169a57PdnGAbDhw9n5syZLFmyhNjY2Ivu407fYXHO70Lu+DNos9nIyMjIc5s7fX/5Kej8LuTq31/Xrl3ZuHEj69atc7yuvPJK7rzzTtatW5cr/ICJ32GZDrGuhKZPn25YrVZj6tSpxpYtW4yhQ4caoaGhRnx8vGEYhnH33XcbTz75pKP977//bnh5eRkTJ040tm7daowZM8bw9vY2Nm7caNYpFKio5zdu3Dhj/vz5xq5du4w1a9YYt912m+Hr62ts3rzZrFMoUHJysrF27Vpj7dq1BmC8/vrrxtq1a419+/YZhmEYTz75pHH33Xc72u/evdvw9/c3Hn/8cWPr1q3Gu+++a3h6ehrz5s0z6xQKVNTze+ONN4xZs2YZO3bsMDZu3GiMGDHC8PDwMBYtWmTWKRTogQceMEJCQoylS5cacXFxjldaWpqjjTv/DBbn/NztZ/DJJ580li1bZuzZs8fYsGGD8eSTTxoWi8VYsGCBYRju/f0ZRtHPz92+v7xceBeYq3yHCkBl4O233zZq1apl+Pj4GK1btzZWrVrl2NaxY0dj0KBBTu2//fZb45JLLjF8fHyMpk2bGj///HM5V1w0RTm/kSNHOtpGRkYavXv3Nv755x8Tqi6cnNu+L3zlnNOgQYOMjh075tqnRYsWho+Pj1G3bl1jypQp5V53YRX1/F5++WWjXr16hq+vrxEeHm506tTJWLJkiTnFF0Je5wY4fSfu/DNYnPNzt5/Be++916hdu7bh4+NjVKtWzejatasjHBiGe39/hlH083O37y8vFwYgV/kOLYZhGGXbxyQiIiLiWjQGSERERCodBSARERGpdBSAREREpNJRABIREZFKRwFIREREKh0FIBEREal0FIBERESk0lEAEhEpBIvFwqxZs8wuQ0RKiQKQiLi8wYMHY7FYcr169uxpdmki4qY0G7yIuIWePXsyZcoUp3VWq9WkakTE3akHSETcgtVqJSoqyukVFhYG2C9PTZ48mV69euHn50fdunX57rvvnPbfuHEjXbp0wc/PjypVqjB06FBSUlKc2nz66ac0bdoUq9VKdHQ0w4cPd9p+/Phxrr/+evz9/WnQoAE//fRT2Z60iJQZBSARqRCeffZZbrzxRtavX8+dd97JbbfdxtatWwFITU2lR48ehIWF8ddffzFjxgwWLVrkFHAmT57MsGHDGDp0KBs3buSnn36ifv36Tp8xbtw4brnlFjZs2EDv3r258847OXnyZLmep4iUkjKfblVEpIQGDRpkeHp6GgEBAU6vF154wTAM+yzp999/v9M+bdq0MR544AHDMAzjww8/NMLCwoyUlBTH9p9//tnw8PAw4uPjDcMwjJiYGOPpp5/OtwbAeOaZZxzvU1JSDMCYO3duqZ2niJQfjQESEbfQuXNnJk+e7LQuPDzcsdy2bVunbW3btmXdunUAbN26lebNmxMQEODY3r59e2w2G9u3b8disXD48GG6du1aYA3NmjVzLAcEBBAcHMzRo0eLe0oiYiIFIBFxCwEBAbkuSZUWPz+/QrXz9vZ2em+xWLDZbGVRkoiUMY0BEpEKYdWqVbneN27cGIDGjRuzfv16UlNTHdt///13PDw8aNiwIUFBQdSpU4fFixeXa80iYh71AImIW8jIyCA+Pt5pnZeXF1WrVgVgxowZXHnllVx99dV89dVX/Pnnn3zyyScA3HnnnYwZM4ZBgwYxduxYjh07xkMPPcTdd99NZGQkAGPHjuX+++8nIiKCXr16kZyczO+//85DDz1UvicqIuVCAUhE3MK8efOIjo52WtewYUO2bdsG2O/Qmj59Og8++CDR0dF8/fXXNGnSBAB/f3/mz5/PiBEjaNWqFf7+/tx44428/vrrjmMNGjSI9PR03njjDR577DGqVq3KTTfdVH4nKCLlymIYhmF2ESIiJWGxWJg5cyYDBgwwuxQRcRMaAyQiIiKVjgKQiIiIVDoaAyQibk9X8kWkqNQDJCIiIpWOApCIiIhUOgpAIiIiUukoAImIiEilowAkIiIilY4CkIiIiFQ6CkAiIiJS6SgAiYiISKWjACQiIiKVzv8DYGZw2o60xpoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw loss values for training & validation\n",
    "plt.plot(history.global_history['activation_46_loss'])\n",
    "plt.plot(history.global_history['val_activation_46_loss'])\n",
    "plt.title('FLModel activation_46_loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw loss values for training & validation\n",
    "plt.plot(history.global_history['activation_53_loss'])\n",
    "plt.plot(history.global_history['val_activation_53_loss'])\n",
    "plt.title('FLModel activation_53_loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw loss values for training & validation\n",
    "plt.plot(history.global_history['activation_46_mae'])\n",
    "plt.plot(history.global_history['val_activation_46_mae'])\n",
    "plt.title('FLModel activation_46_mae')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw loss values for training & validation\n",
    "plt.plot(history.global_history['activation_53_mae'])\n",
    "plt.plot(history.global_history['val_activation_53_mae'])\n",
    "plt.title('FLModel activation_46_mae')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "basnet_segmentation",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
